system

Abstract

The system according to this embodiment aims to estimate the emotions of tourists and propose an optimal sightseeing plan based on that estimation. [Solution] The system according to the embodiment comprises an acquisition unit, an emotion estimation unit, a decision unit, and a proposal unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of tourists. The emotion estimation unit estimates the emotions of tourists based on the emotion estimation information acquired by the acquisition unit. The decision unit determines a sightseeing plan based on the emotions estimated by the emotion estimation unit. The proposal unit proposes the sightseeing plan determined by the decision unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a tourism plan has not been sufficiently proposed based on the emotions of tourists, and there is room for improvement.

[0005] The system according to the embodiment aims to estimate the emotions of tourists and propose an optimal tourism plan based on the emotions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an emotion estimation unit, a decision unit, and a proposal unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of tourists. The emotion estimation unit estimates the emotions of tourists based on the emotion estimation information acquired by the acquisition unit. The decision unit determines a sightseeing plan based on the emotions estimated by the emotion estimation unit. The proposal unit proposes the sightseeing plan determined by the decision unit. [Effects of the Invention]

[0007] The system according to this embodiment can estimate the emotions of tourists and propose an optimal sightseeing plan based on that. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The tourist information robot according to an embodiment of the present invention is a system that estimates the emotions of tourists and proposes a sightseeing plan based on those emotions and the tourists' interests and needs. The tourist information robot acquires information for estimating the emotions of tourists (emotion estimation information). This information includes data on the tourists' facial expressions, voice, and behavior. Next, it estimates the emotions of tourists based on the acquired information. For example, if the tourist is tired, it suggests relaxing spots or places suitable for resting. Also, if the tourist is sad or depressed, it suggests spots or events that will brighten their mood. The sightseeing plan includes sightseeing locations, sightseeing routes, types of restaurants, etc. Furthermore, if the tourists are in a group, it estimates the emotions of multiple tourists belonging to the group and proposes a joint sightseeing plan based on the emotions, interests, and needs of the multiple tourists. This can improve the quality of the sightseeing experience and increase visitor satisfaction. The tourist information robot comprises an acquisition unit that acquires information used to estimate the emotions of tourists, an emotion estimation unit that estimates the emotions of tourists based on the acquired information, a decision unit that determines a sightseeing plan based on the estimated emotions, and a proposal unit that proposes the determined sightseeing plan. Furthermore, it is equipped with a reception area that receives information on tourists' needs and interests, and determines a sightseeing plan based on the emotions estimated by the emotion estimation unit and the information received by the reception area. If the tourists are a group, the emotion estimation unit estimates the emotions of multiple tourists, and the decision unit determines a joint sightseeing plan based on the emotions of the multiple tourists. In this way, the sightseeing robot can improve the quality of the sightseeing experience and increase visitor satisfaction by proposing sightseeing plans based on the emotions of tourists.

[0029] The tourist information robot according to this embodiment comprises an acquisition unit, an emotion estimation unit, a decision unit, and a suggestion unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of tourists. Emotion estimation information includes, for example, the tourist's facial expression data, voice data, and behavioral data. The acquisition unit can, for example, use a camera to photograph the tourist's facial expression and acquire facial expression data. The acquisition unit can also record the tourist's voice using a microphone and acquire voice data. Furthermore, the acquisition unit can detect the tourist's behavior with sensors and acquire behavioral data. For example, the acquisition unit detects the tourist's walking pattern and collects it as behavioral data. The emotion estimation unit estimates the tourist's emotions based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit can, for example, use facial expression recognition technology to estimate emotions from the tourist's facial expression data. Furthermore, the emotion estimation unit can also use voice analysis technology to estimate emotions from the tourist's voice data. Furthermore, the emotion estimation unit can also use behavioral analysis technology to estimate emotions from the tourist's behavioral data. For example, the emotion estimation unit takes the tourist's facial expression data as input and estimates their emotions using an AI model that outputs emotions. The decision unit determines a sightseeing plan based on the emotions estimated by the emotion estimation unit. For example, if the tourist is relaxed, the decision unit determines a sightseeing plan that includes relaxing tourist spots. The decision unit can also determine a sightseeing plan that includes exciting tourist spots if the tourist is excited. Furthermore, if the tourist is tired, the decision unit can also determine a sightseeing plan that includes rest. For example, the decision unit takes the tourist's emotion data as input and determines a sightseeing plan using an AI model that outputs sightseeing plans. The suggestion unit proposes the sightseeing plan determined by the decision unit. The suggestion unit can, for example, display the sightseeing plan on a screen. The suggestion unit can also provide the sightseeing plan via voice. Furthermore, the suggestion unit can print and provide the sightseeing plan. For example, the suggestion unit can output the sightseeing plan as text data and display it on a screen. As a result, the sightseeing guidance robot according to this embodiment can improve the quality of the sightseeing experience and increase visitor satisfaction by proposing a sightseeing plan based on the tourist's emotions.

[0030] The data acquisition unit acquires emotion estimation information, which is used to estimate the emotions of tourists. This emotion estimation information includes, for example, facial expression data, voice data, and behavioral data of tourists. For example, the data acquisition unit can use a camera to photograph tourists' facial expressions and acquire facial expression data. Specifically, the camera has high resolution and can capture subtle changes in facial expressions. The data acquisition unit can also use a microphone to record tourists' voices and acquire voice data. The microphone has a noise-canceling function to remove ambient noise, allowing for clear recording of tourists' voices. Furthermore, the data acquisition unit can detect tourists' behavior with sensors and acquire behavioral data. For example, the data acquisition unit can detect tourists' walking patterns and collect them as behavioral data. Walking pattern data is acquired using acceleration sensors and gyroscope sensors and is important information for estimating the tourist's fatigue level and excitement level. This allows the data acquisition unit to efficiently collect information necessary for estimating tourists' emotions from a variety of data sources. Furthermore, the data acquisition unit can process this data in real time and provide it quickly to the emotion estimation unit. This will enable tourist information robots to provide services that respond immediately to the emotions of tourists.

[0031] The emotion estimation unit estimates the tourist's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit estimates emotions from the tourist's facial expression data using facial recognition technology. Specifically, facial recognition technology analyzes facial feature points to identify emotions such as smiling, surprise, and anger. The emotion estimation unit can also estimate emotions from the tourist's voice data using voice analysis technology. Voice analysis technology analyzes the tone, pitch, and speed of the voice to estimate the tourist's emotional state. Furthermore, the emotion estimation unit can also estimate emotions from the tourist's behavioral data using behavioral analysis technology. For example, the emotion estimation unit analyzes the tourist's walking pattern and movement speed to estimate their fatigue level and excitement level. The emotion estimation unit integrates this data and uses an AI model to estimate the tourist's overall emotional state with high accuracy. Specifically, an AI model using deep learning receives facial expression data, voice data, and behavioral data as input and outputs emotions. This AI model has been trained using a large amount of training data and can estimate emotions with high accuracy. This allows the emotion estimation unit to accurately grasp tourists' emotions in real time and provide the information necessary to decide on the next step, which is the sightseeing plan.

[0032] The decision-making unit determines a sightseeing plan based on the emotions estimated by the emotion estimation unit. For example, if the tourist is relaxed, the decision-making unit will determine a sightseeing plan that includes relaxing spots. Specifically, if the tourist is estimated to be relaxed, it will suggest a plan that includes relaxing places such as nature parks, hot springs, and quiet cafes. The decision-making unit can also determine a sightseeing plan that includes exciting spots if the tourist is excited. For example, it will suggest a plan that includes exciting places such as theme parks, adventure sports, and live events. Furthermore, if the tourist is tired, the decision-making unit can determine a sightseeing plan that includes rest areas. For example, if the tourist is estimated to be tired, it will suggest a plan that includes rest areas and relaxation facilities. The decision-making unit determines the sightseeing plan using an AI model that takes the tourist's emotion data as input and outputs a sightseeing plan. This AI model is linked to a database of tourist spots and can select the most suitable spots for the tourist's emotional state. In addition, the decision-making unit can provide a more personalized sightseeing plan by considering the tourist's past behavioral history and preferences. As a result, the decision-making unit can quickly determine the optimal sightseeing plan according to the tourist's emotions and improve the quality of the sightseeing experience.

[0033] The proposal unit proposes the sightseeing plan determined by the decision unit. For example, the proposal unit displays the sightseeing plan on a screen. Specifically, it displays photos, descriptions, and maps of tourist spots on the display of a sightseeing robot, providing visually easy-to-understand guidance. The proposal unit can also provide the sightseeing plan via voice guidance. Voice guidance is user-friendly and conveys information intuitively to tourists. Furthermore, the proposal unit can provide the sightseeing plan in print. For example, the sightseeing plan can be output as text data, printed, and handed to tourists. This allows tourists to carry paper maps and guides, improving convenience. The proposal unit can also customize the method of proposing the sightseeing plan according to the tourist's preferences. For example, it can prioritize voice guidance for visually impaired tourists and emphasize display information for hearing-impaired tourists, providing proposals tailored to individual needs. Furthermore, the proposal unit can collect feedback from tourists and continuously improve the accuracy and effectiveness of the proposals. For example, it can record how tourists reacted to the proposed plan and reflect this in future proposals. This allows the proposal department to provide tourists with optimal sightseeing plans and enhance the quality of their sightseeing experience.

[0034] The tourist information robot is equipped with a reception desk that receives information about tourists' needs or interests. The reception desk receives information about tourists' needs and interests. For example, the reception desk collects information about tourists' needs and interests through questionnaires. The reception desk can also estimate tourists' needs and interests based on their past behavioral history. Furthermore, the reception desk can also estimate tourists' needs and interests by observing their current behavior. For example, the reception desk collects data on places visited and activities participated in by tourists to estimate their needs and interests. This allows the tourist information robot to provide a more personalized tourist experience by proposing sightseeing plans based on tourists' needs and interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input tourist questionnaire data into AI and have the AI ​​perform the estimation of needs and interests.

[0035] The decision unit determines a sightseeing plan based on the emotions estimated by the emotion estimation unit and the information received by the reception unit. For example, if the tourist is relaxed, the decision unit will determine a sightseeing plan that includes relaxing sightseeing spots. The decision unit can also determine a sightseeing plan that includes exciting sightseeing spots if the tourist is excited. Furthermore, if the tourist is tired, the decision unit can also determine a sightseeing plan that includes rest. For example, the decision unit determines a sightseeing plan using an AI model that takes the tourist's emotion data and needs and interests data as input and outputs a sightseeing plan. This allows the sightseeing robot to provide a more appropriate sightseeing plan by determining the sightseeing plan based on the tourist's emotions and needs and interests. Some or all of the above processing in the decision unit may be performed using AI or not. For example, the decision unit can input the tourist's emotion data and needs and interests data into an AI and have the AI ​​perform the sightseeing plan determination.

[0036] The emotion estimation unit estimates the emotions of multiple tourists belonging to a group when the tourists are in a group. For example, the emotion estimation unit collects facial expression data, voice data, and behavioral data from each tourist in the group and estimates the emotion of each tourist. The emotion estimation unit can also estimate the emotion of the entire group based on the emotion data of each tourist. For example, the emotion estimation unit averages the emotion data of each tourist in the group to estimate the emotion of the entire group. This allows the tourist guidance robot to provide a tourist plan suitable for the entire group by estimating the emotions of multiple tourists belonging to the group. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion estimation unit may be performed using AI or not using AI. For example, the emotion estimation unit can input the emotion data of each tourist in the group into the AI ​​and have the AI ​​perform the estimation of the emotion of the entire group.

[0037] The decision unit determines a joint sightseeing plan based on the emotions of multiple tourists estimated by the emotion estimation unit. For example, the decision unit determines a sightseeing plan that includes sightseeing spots that everyone can enjoy, based on the emotional data of each tourist in the group. The decision unit can also determine a sightseeing plan that includes places of common interest, based on the emotional data of each tourist in the group. Furthermore, the decision unit can determine a sightseeing plan that allows everyone to relax, based on the emotional data of each tourist in the group. For example, the decision unit can input the emotional data of each tourist in the group into an AI and have the AI ​​perform the determination of the joint sightseeing plan. In this way, the sightseeing robot improves the overall satisfaction of the group by determining a joint sightseeing plan based on the emotions of multiple tourists. Some or all of the above processing in the decision unit may be performed using AI or not using AI.

[0038] The acquisition unit collects tourist facial expression data, voice data, and behavioral data as information for emotion estimation. For example, the acquisition unit can use a camera to photograph the tourist's facial expression and collect facial expression data. The acquisition unit can also use a microphone to record the tourist's voice and collect voice data. Furthermore, the acquisition unit can detect the tourist's behavior with sensors and collect behavioral data. For example, the acquisition unit can detect the tourist's walking pattern and collect it as behavioral data. This allows the tourist guidance robot to perform more accurate emotion estimation by collecting data on the tourist's facial expression, voice, and behavior. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the tourist's facial expression data into an AI and have the AI ​​perform the collection of emotion estimation information.

[0039] A sightseeing plan includes sightseeing locations, sightseeing routes, and restaurant genres. For example, a sightseeing plan might include recommended tourist spots, efficient sightseeing routes, and restaurant genres offering local specialties. The sightseeing plan is customized based on the tourist's emotions and needs. For instance, if a tourist wants to relax, a plan including relaxing sightseeing spots and restaurants might be suggested. If the tourist wants an active sightseeing experience, a plan including activity-rich sightseeing spots and restaurants could be suggested. Furthermore, if the tourist is seeking a cultural experience, a plan including historical sites and restaurants serving traditional cuisine could be suggested. This allows the sightseeing robot to provide a comprehensive sightseeing experience by including sightseeing locations, sightseeing routes, and restaurant genres in the sightseeing plan. Some or all of the above processes in determining the sightseeing plan may be performed using AI or not. For example, in determining the sightseeing plan, tourist emotion data and data on needs and interests could be input into the AI, allowing the AI ​​to perform the sightseeing plan determination.

[0040] The acquisition unit estimates the tourist's emotions and adjusts the timing of acquiring emotion estimation information based on the estimated emotions. For example, if the tourist is relaxed, the acquisition unit acquires emotion estimation information immediately after they arrive at the tourist site. If the tourist is excited, the acquisition unit can also acquire emotion estimation information immediately after they see the highlights of the tourist site. Furthermore, if the tourist is tired, the acquisition unit can acquire emotion estimation information during breaks or meals. For example, the acquisition unit acquires emotion estimation information at the optimal timing based on the tourist's emotion data. This allows the tourist guidance robot to acquire information at a more appropriate time by adjusting the timing of acquiring emotion estimation information based on the tourist's emotions. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the tourist's emotion data into AI and have the AI ​​adjust the timing of acquiring emotion estimation information.

[0041] The acquisition unit analyzes the tourist's past behavioral history and selects the optimal acquisition method. For example, the acquisition unit selects a method for acquiring sentiment estimation information based on data of places the tourist has visited in the past. The acquisition unit can also analyze the tourist's past travel patterns and acquire sentiment estimation information at the optimal timing. Furthermore, the acquisition unit can select a method for acquiring sentiment estimation information at a specific location based on the tourist's past behavioral history. For example, the acquisition unit can input the tourist's past behavioral data into an AI and have the AI ​​select the optimal acquisition method. This allows the tourist information robot to select the optimal acquisition method by analyzing the tourist's past behavioral history. Some or all of the above processing in the acquisition unit may be performed using AI or not.

[0042] The acquisition unit filters the sentiment estimation information based on the tourist's current activities and areas of interest when acquiring it. For example, if the tourist is actively sightseeing, the acquisition unit acquires sentiment estimation information related to dynamic activities. The acquisition unit can also acquire sentiment estimation information related to cultural interests if the tourist is visiting a cultural site. Furthermore, if the tourist is enjoying shopping, the acquisition unit can acquire sentiment estimation information related to shopping. For example, the acquisition unit can input the tourist's current activities and areas of interest into an AI and have the AI ​​perform the filtering of sentiment estimation information. This allows the tourist information robot to acquire more relevant information by filtering the information based on the tourist's current activities and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI or not.

[0043] The acquisition unit estimates the tourist's emotions and determines the priority of information to acquire based on the estimated emotions. For example, if the tourist is excited, the acquisition unit prioritizes acquiring information related to the factors causing the excitement. The acquisition unit can also prioritize acquiring information related to relaxation if the tourist is relaxed. Furthermore, if the tourist is tired, the acquisition unit can prioritize acquiring information related to rest. For example, the acquisition unit can input the tourist's emotion data into an AI and have the AI ​​determine the priority of information to acquire. This allows the tourist information robot to prioritize acquiring important information by determining the priority of information based on the tourist's emotions. Some or all of the above processing in the acquisition unit may be performed using AI or not.

[0044] The acquisition unit prioritizes acquiring highly relevant information when acquiring information for sentiment estimation, taking into account the tourist's geographical location. For example, if the tourist is in a specific tourist destination, the acquisition unit prioritizes acquiring information related to that destination. The acquisition unit can also prioritize acquiring information related to a specific area if the tourist is in that area. Furthermore, if the tourist is on the move, the acquisition unit can prioritize acquiring information related to the next destination. For example, the acquisition unit can input the tourist's geographical location into an AI and have the AI ​​prioritize acquiring highly relevant information. This allows the tourist information robot to prioritize acquiring highly relevant information by taking the tourist's geographical location into consideration. Some or all of the above processing in the acquisition unit may be performed using AI or not.

[0045] The acquisition unit analyzes the tourist's social media activity and obtains relevant information when acquiring information for sentiment estimation. For example, the acquisition unit analyzes photos and posts shared by the tourist on social media and obtains relevant sentiment estimation information. The acquisition unit can also acquire relevant sentiment estimation information based on information about accounts that the tourist follows on social media. Furthermore, the acquisition unit can also acquire relevant sentiment estimation information based on information about places that the tourist checked in to on social media. For example, the acquisition unit can input the tourist's social media activity into an AI and have the AI ​​acquire the relevant information. This allows the tourist information robot to acquire relevant information by analyzing the tourist's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI or not using AI.

[0046] The emotion estimation unit estimates the tourist's emotions and adjusts the emotion estimation algorithm based on the estimated emotions. For example, if the tourist is relaxed, the emotion estimation unit applies an emotion estimation algorithm related to relaxation. The emotion estimation unit can also apply an emotion estimation algorithm related to excitement if the tourist is excited. Furthermore, if the tourist is tired, the emotion estimation unit can apply an emotion estimation algorithm related to fatigue. For example, the emotion estimation unit can input the tourist's emotion data into an AI and have the AI ​​adjust the emotion estimation algorithm. This allows the tourist guidance robot to improve its estimation accuracy by adjusting the emotion estimation algorithm based on the tourist's emotions. Some or all of the above processing in the emotion estimation unit may be performed using or without AI.

[0047] The emotion estimation unit improves estimation accuracy by referring to the tourist's past emotion data during emotion estimation. For example, the emotion estimation unit estimates the tourist's current emotion based on the tourist's past emotion data. The emotion estimation unit can also analyze the tourist's past emotion patterns to improve estimation accuracy. Furthermore, the emotion estimation unit can adjust the emotion estimation algorithm by referring to the tourist's past emotion data. For example, the emotion estimation unit can input the tourist's past emotion data into an AI and have the AI ​​perform the improvement of estimation accuracy. As a result, the tourist guidance robot improves estimation accuracy by referring to the tourist's past emotion data. Some or all of the above processing in the emotion estimation unit may be performed using AI or not using AI.

[0048] The emotion estimation unit considers the tourist's attribute information (age, gender, etc.) when estimating emotions. For example, the emotion estimation unit adjusts the emotion estimation algorithm based on the tourist's age. It can also adjust the emotion estimation algorithm based on the tourist's gender. Furthermore, the emotion estimation unit can improve the accuracy of emotion estimation based on the tourist's attribute information. For example, the emotion estimation unit can input the tourist's attribute information into an AI and have the AI ​​perform the task of improving the accuracy of emotion estimation. As a result, the tourist information robot improves its estimation accuracy by considering the tourist's attribute information. Some or all of the above processing in the emotion estimation unit may be performed using AI or not.

[0049] The emotion estimation unit estimates the tourist's emotions and adjusts the display method of the estimation results based on the estimated emotions of the tourist. For example, if the tourist is relaxed, the emotion estimation unit provides a display method related to relaxation. The emotion estimation unit can also provide a display method related to excitement if the tourist is excited. Furthermore, if the tourist is tired, the emotion estimation unit can also provide a display method related to fatigue. For example, the emotion estimation unit can input the tourist's emotion data into an AI and have the AI ​​adjust the display method of the estimation results. This allows the tourist information robot to provide more appropriate displays by adjusting the display method of the estimation results based on the tourist's emotions. Some or all of the above processing in the emotion estimation unit may be performed using AI or not using AI.

[0050] The emotion estimation unit considers the geographical distribution of tourists when estimating emotions. For example, if a tourist is in a specific area, the emotion estimation unit applies an emotion estimation algorithm relevant to that area. The emotion estimation unit can also improve the accuracy of emotion estimation based on the geographical distribution of tourists. Furthermore, the emotion estimation unit can adjust the emotion estimation algorithm considering the geographical distribution of tourists. For example, the emotion estimation unit can input the geographical distribution of tourists into an AI and have the AI ​​perform an improvement in the accuracy of emotion estimation. This allows the tourist information robot to improve its estimation accuracy by considering the geographical distribution of tourists. Some or all of the above processing in the emotion estimation unit may be performed using AI or not.

[0051] The sentiment estimation unit improves estimation accuracy by referring to relevant literature on tourists during sentiment estimation. For example, the sentiment estimation unit adjusts the sentiment estimation algorithm based on relevant literature on tourists. The sentiment estimation unit can also improve the accuracy of sentiment estimation by referring to relevant literature on tourists. Furthermore, the sentiment estimation unit can correct the results of sentiment estimation based on relevant literature on tourists. For example, the sentiment estimation unit can input relevant literature on tourists into an AI and have the AI ​​perform the improvement of estimation accuracy. As a result, the tourist information robot improves estimation accuracy by referring to relevant literature on tourists. Some or all of the above processing in the sentiment estimation unit may be performed using AI or not using AI.

[0052] The decision-making unit estimates the tourist's emotions and adjusts the method of determining the sightseeing plan based on the estimated emotions. For example, if the tourist is relaxed, the decision-making unit will prioritize a relaxing sightseeing plan. If the tourist is excited, the decision-making unit may also prioritize a sightseeing plan that will cause excitement. Furthermore, if the tourist is tired, the decision-making unit may also prioritize a sightseeing plan that includes rest. For example, the decision-making unit can input the tourist's emotion data into an AI and have the AI ​​adjust the method of determining the sightseeing plan. This allows the sightseeing robot to provide a more appropriate sightseeing plan by adjusting the method of determining the sightseeing plan based on the tourist's emotions. Some or all of the above processing in the decision-making unit may be performed using AI or not.

[0053] The decision-making unit selects the optimal plan when determining a sightseeing plan by referring to the tourist's past sightseeing history. For example, the decision-making unit selects the optimal sightseeing plan based on the tourist's past sightseeing history. The decision-making unit can also analyze the tourist's past sightseeing patterns and select the optimal sightseeing plan. Furthermore, the decision-making unit can improve the accuracy of sightseeing plan selection by referring to the tourist's past sightseeing history. For example, the decision-making unit can input the tourist's past sightseeing history into an AI and have the AI ​​perform the selection of the optimal sightseeing plan. This allows the sightseeing guidance robot to select the optimal sightseeing plan by referring to the tourist's past sightseeing history. Some or all of the above processing in the decision-making unit may be performed using AI or not using AI.

[0054] The decision-making unit customizes the sightseeing plan based on the tourist's current activities when determining the plan. For example, if the tourist is actively sightseeing, the decision-making unit will customize the sightseeing plan to include dynamic activities. The decision-making unit can also customize the sightseeing plan to relate to cultural interests if the tourist is visiting cultural sites. Furthermore, if the tourist is enjoying shopping, the decision-making unit can also customize the sightseeing plan to relate to shopping. For example, the decision-making unit can input the tourist's current activities into the AI ​​and have the AI ​​perform the customization of the sightseeing plan. This allows the sightseeing robot to provide a more appropriate sightseeing plan by customizing the plan based on the tourist's current activities. Some or all of the above processing in the decision-making unit may be performed using AI or not.

[0055] The decision unit estimates the tourist's emotions and determines the priority of sightseeing plans based on the estimated emotions. For example, if the tourist is relaxed, the decision unit will prioritize sightseeing plans that promote relaxation. The decision unit can also prioritize sightseeing plans that cause excitement if the tourist is excited. Furthermore, if the tourist is tired, the decision unit can prioritize sightseeing plans that include rest. For example, the decision unit can input the tourist's emotion data into an AI and have the AI ​​perform the task of determining the priority of sightseeing plans. This allows the sightseeing robot to prioritize important plans by determining the priority of sightseeing plans based on the tourist's emotions. Some or all of the above processing in the decision unit may be performed using AI or not.

[0056] The decision-making unit selects the optimal plan when determining a sightseeing plan, taking into account the tourist's geographical location. For example, if the tourist is in a specific tourist destination, the decision-making unit will prioritize selecting a sightseeing plan related to that destination. The decision-making unit can also prioritize selecting a sightseeing plan related to a specific area if the tourist is in that area. Furthermore, if the tourist is on the move, the decision-making unit can also prioritize selecting a sightseeing plan related to the next destination. For example, the decision-making unit can input the tourist's geographical location into an AI and have the AI ​​select the optimal sightseeing plan. This allows the sightseeing robot to select the optimal sightseeing plan by taking the tourist's geographical location into consideration. Some or all of the above processing in the decision-making unit may be performed using AI or not.

[0057] The decision-making unit analyzes the tourist's social media activity and proposes a plan when determining a sightseeing plan. For example, the decision-making unit analyzes photos and posts shared by the tourist on social media and proposes a relevant sightseeing plan. The decision-making unit can also propose a relevant sightseeing plan based on information about accounts the tourist follows on social media. Furthermore, the decision-making unit can propose a relevant sightseeing plan based on information about places the tourist has checked in to on social media. For example, the decision-making unit can input the tourist's social media activity into an AI and have the AI ​​propose a sightseeing plan. This allows the sightseeing guidance robot to propose a relevant sightseeing plan by analyzing the tourist's social media activity. Some or all of the above processing in the decision-making unit may be performed using AI or not.

[0058] The suggestion unit estimates the tourist's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the tourist is relaxed, the suggestion unit will present suggestions in a way that promotes relaxation. If the tourist is excited, the suggestion unit can also present suggestions in a way that evokes excitement. Furthermore, if the tourist is tired, the suggestion unit can present suggestions that include the idea of ​​taking a break. For example, the suggestion unit can input the tourist's emotion data into an AI and have the AI ​​adjust the way it presents suggestions. This allows the tourist information robot to make more appropriate suggestions by adjusting the way it presents suggestions based on the tourist's emotions. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0059] The suggestion unit adjusts the level of detail in its suggestions based on the importance of the sightseeing plan. For example, if a tourist spot is important, the suggestion unit will provide a suggestion with detailed information. If a tourist spot is less important, the suggestion unit may provide a suggestion with concise information. Furthermore, the suggestion unit can also adjust the level of detail in its suggestions based on the overall importance of the sightseeing plan. For example, the suggestion unit can input the importance of the sightseeing plan into the AI ​​and have the AI ​​adjust the level of detail in its suggestions. This allows the sightseeing robot to provide more appropriate information by adjusting the level of detail in its suggestions based on the importance of the sightseeing plan. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0060] The suggestion unit applies different suggestion algorithms depending on the category of the tourist plan when making suggestions. For example, in the case of a cultural tourist spot, the suggestion unit applies a suggestion algorithm related to culture. It can also apply a suggestion algorithm related to nature in the case of a natural tourist spot. Furthermore, it can apply a suggestion algorithm related to shopping in the case of a shopping spot. For example, the suggestion unit can input the category of the tourist plan into the AI ​​and have the AI ​​apply the suggestion algorithm. This allows the tourist information robot to make more appropriate suggestions by applying different suggestion algorithms depending on the category of the tourist plan. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0061] The suggestion unit estimates the tourist's emotions and adjusts the length of the suggestions based on the estimated emotions. For example, if the tourist is relaxed, the suggestion unit will provide detailed suggestions. If the tourist is excited, the suggestion unit can also provide concise suggestions. Furthermore, if the tourist is tired, the suggestion unit can provide short suggestions. For example, the suggestion unit can input the tourist's emotion data into an AI and have the AI ​​adjust the length of the suggestions. This allows the tourist guidance robot to provide more appropriate suggestions by adjusting the length of the suggestions based on the tourist's emotions. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0062] The proposal department determines the priority of proposals based on the submission timing of the tourism plan. For example, if the tourism plan is submitted early, the proposal department will prioritize it. Conversely, if the tourism plan is submitted late, the proposal department may postpone it. Furthermore, the proposal department can adjust the priority of proposals based on the submission timing. For example, the proposal department can input the tourism plan submission timing into the AI ​​and have the AI ​​determine the priority of proposals. This allows the tourism guidance robot to make proposals at a more appropriate time by determining the priority of proposals based on the tourism plan submission timing. Some or all of the above processing in the proposal department may be performed using AI or not.

[0063] The suggestion unit adjusts the order of suggestions based on the relevance of the sightseeing plans. For example, the suggestion unit prioritizes suggestions that are highly relevant to the sightseeing plans. It can also postpone suggestions that are less relevant to the sightseeing plans. Furthermore, the suggestion unit can adjust the order of suggestions based on their relevance. For example, the suggestion unit can input the relevance of the sightseeing plans into an AI and have the AI ​​adjust the order of suggestions. This allows the sightseeing guidance robot to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of the sightseeing plans. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0064] The reception desk estimates the tourist's emotions and adjusts the reception method based on the estimated emotions. For example, if the tourist is relaxed, the reception desk provides a relaxing reception method. If the tourist is excited, the reception desk can also provide a reception method that excites them. Furthermore, if the tourist is tired, the reception desk can provide a reception method that includes resting. For example, the reception desk can input the tourist's emotion data into an AI and have the AI ​​adjust the reception method. This allows the tourist information robot to provide more appropriate reception by adjusting the reception method based on the tourist's emotions. Some or all of the above processing in the reception desk may be performed using AI or not.

[0065] The reception desk selects the optimal reception method by referring to data on the tourist's past needs and interests at the time of check-in. For example, the reception desk can select the optimal reception method based on the tourist's past needs. The reception desk can also select the optimal reception method based on the tourist's past interests. Furthermore, the reception desk can adjust the reception method by referring to the tourist's past data. For example, the reception desk can input data on the tourist's past needs and interests into an AI and have the AI ​​select the optimal reception method. This allows the tourist information robot to select the optimal reception method by referring to data on the tourist's past needs and interests. Some or all of the above processing in the reception desk may be performed using AI or not using AI.

[0066] The reception desk estimates the emotions of tourists and determines the priority of services based on the estimated emotions. For example, if a tourist is relaxed, the reception desk will prioritize services that promote relaxation. If a tourist is excited, the reception desk may also prioritize services that would cause excitement. Furthermore, if a tourist is tired, the reception desk may also prioritize services that include rest. For example, the reception desk can input tourist emotion data into an AI and have the AI ​​determine the priority of services. This allows the tourist information robot to provide more appropriate services by prioritizing services based on the emotions of tourists. Some or all of the above processing in the reception desk may be performed using AI or not.

[0067] The reception desk selects the most appropriate reception method at the time of check-in, taking into account the tourist's geographical location. For example, if the tourist is at a specific tourist destination, the reception desk provides a reception method relevant to that destination. The reception desk can also provide a reception method relevant to a specific area if the tourist is in that area. Furthermore, if the tourist is on the move, the reception desk can provide a reception method relevant to their next destination. For example, the reception desk can input the tourist's geographical location into an AI and have the AI ​​select the most appropriate reception method. This allows the tourist information robot to select the most appropriate reception method by taking the tourist's geographical location into account. Some or all of the above processing at the reception desk may be performed using AI or not.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The tourist information robot can monitor the congestion levels of tourist destinations in real time based on tourists' interests and needs, and propose sightseeing plans that avoid crowds. For example, if a tourist prefers quiet places, it can suggest less crowded tourist spots. If a tourist wants to visit a popular tourist spot, it can also suggest less crowded times. This allows the tourist information robot to improve the quality of the sightseeing experience by proposing sightseeing plans that take congestion levels into account. Furthermore, it can dynamically adjust sightseeing plans based on real-time congestion monitoring. For example, if a tourist spot suddenly becomes crowded, it can change the destination to a different tourist spot. This allows tourists to enjoy a more comfortable sightseeing experience.

[0070] The tourist information robot can collect local event information in real time based on tourists' interests and needs and incorporate it into their travel plans. For example, if a tourist is interested in music events, it can provide information on local concerts and festivals. If a tourist is interested in food, it can provide information on local food festivals and markets. This allows the tourist information robot to improve the quality of the travel experience by suggesting travel plans that take local events into account. Furthermore, it can collect local event information in real time and dynamically adjust travel plans. For example, if an interesting event is suddenly announced, it can be incorporated into the travel plan. This allows tourists to enjoy local culture and events.

[0071] The tourist information robot can monitor weather information at tourist destinations in real time based on tourists' interests and needs, and propose sightseeing plans that are appropriate for the weather. For example, if tourists prefer outdoor activities, it can suggest outdoor sightseeing spots on days with good weather. Conversely, if tourists prefer indoor activities, it can suggest indoor sightseeing spots on days with bad weather. In this way, the tourist information robot can improve the quality of the sightseeing experience by proposing sightseeing plans that take weather information into account. Furthermore, it can also dynamically adjust sightseeing plans by monitoring weather information in real time. For example, if the weather suddenly deteriorates, it can change to indoor sightseeing spots. This allows tourists to enjoy a comfortable sightseeing experience.

[0072] The tourist information robot can monitor traffic information in real time at tourist destinations based on tourists' interests and needs, and suggest the optimal travel route. For example, if a tourist wants to visit tourist spots efficiently, it can suggest a route that avoids traffic congestion. It can also suggest the best bus or train routes if the tourist wants to use public transportation. This allows the tourist information robot to improve the quality of the tourist experience by suggesting sightseeing plans that take traffic information into account. Furthermore, it can dynamically adjust sightseeing plans by monitoring traffic information in real time. For example, if traffic congestion suddenly occurs, it can change to an alternative route. This allows tourists to visit tourist destinations smoothly.

[0073] The tourist information robot can monitor safety information at tourist destinations in real time based on tourists' interests and needs, and propose safe sightseeing plans. For example, if a tourist prefers safe places, it can suggest tourist spots in areas with low crime rates. Similarly, if a tourist wants to avoid the risk of natural disasters, it can suggest tourist spots in areas with low disaster risk. This allows the tourist information robot to improve the quality of the sightseeing experience by proposing sightseeing plans that take safety information into account. Furthermore, it can dynamically adjust sightseeing plans based on real-time monitoring of safety information. For example, if safety risks suddenly increase, it can switch to a different, safer tourist spot. This allows tourists to enjoy sightseeing with peace of mind.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of tourists. Emotion estimation information includes, for example, the tourist's facial expression data, voice data, and behavioral data. The acquisition unit can, for example, use a camera to photograph the tourist's facial expression and acquire facial expression data. The acquisition unit can also use a microphone to record the tourist's voice and acquire voice data. Furthermore, the acquisition unit can detect the tourist's behavior with sensors and acquire behavioral data. For example, the acquisition unit can detect the tourist's walking pattern and collect it as behavioral data. Step 2: The emotion estimation unit estimates the tourist's emotions based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit can, for example, estimate emotions from the tourist's facial expression data using facial expression recognition technology. The emotion estimation unit can also estimate emotions from the tourist's voice data using voice analysis technology. Furthermore, the emotion estimation unit can also estimate emotions from the tourist's behavior data using behavior analysis technology. For example, the emotion estimation unit can estimate emotions using an AI model that takes the tourist's facial expression data as input and outputs emotions. Step 3: The decision unit determines a sightseeing plan based on the emotions estimated by the emotion estimation unit. For example, if the tourist is relaxed, the decision unit will determine a sightseeing plan that includes relaxing sightseeing spots. The decision unit can also determine a sightseeing plan that includes exciting sightseeing spots if the tourist is excited. Furthermore, if the tourist is tired, the decision unit can also determine a sightseeing plan that includes rest. For example, the decision unit determines a sightseeing plan using an AI model that takes the tourist's emotion data as input and outputs a sightseeing plan. Step 4: The proposal unit proposes the sightseeing plan determined by the decision unit. The proposal unit can, for example, display the sightseeing plan on a screen. The proposal unit can also provide audio guidance on the sightseeing plan. Furthermore, the proposal unit can provide the sightseeing plan in print. For example, the proposal unit can output the sightseeing plan as text data and display it on a screen.

[0076] (Example of form 2) The tourist information robot according to an embodiment of the present invention is a system that estimates the emotions of tourists and proposes a sightseeing plan based on those emotions and the tourists' interests and needs. The tourist information robot acquires information for estimating the emotions of tourists (emotion estimation information). This information includes data on the tourists' facial expressions, voice, and behavior. Next, it estimates the emotions of tourists based on the acquired information. For example, if the tourist is tired, it suggests relaxing spots or places suitable for resting. Also, if the tourist is sad or depressed, it suggests spots or events that will brighten their mood. The sightseeing plan includes sightseeing locations, sightseeing routes, types of restaurants, etc. Furthermore, if the tourists are in a group, it estimates the emotions of multiple tourists belonging to the group and proposes a joint sightseeing plan based on the emotions, interests, and needs of the multiple tourists. This can improve the quality of the sightseeing experience and increase visitor satisfaction. The tourist information robot comprises an acquisition unit that acquires information used to estimate the emotions of tourists, an emotion estimation unit that estimates the emotions of tourists based on the acquired information, a decision unit that determines a sightseeing plan based on the estimated emotions, and a proposal unit that proposes the determined sightseeing plan. Furthermore, it is equipped with a reception area that receives information on tourists' needs and interests, and determines a sightseeing plan based on the emotions estimated by the emotion estimation unit and the information received by the reception area. If the tourists are a group, the emotion estimation unit estimates the emotions of multiple tourists, and the decision unit determines a joint sightseeing plan based on the emotions of the multiple tourists. In this way, the sightseeing robot can improve the quality of the sightseeing experience and increase visitor satisfaction by proposing sightseeing plans based on the emotions of tourists.

[0077] The tourist information robot according to this embodiment comprises an acquisition unit, an emotion estimation unit, a decision unit, and a suggestion unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of tourists. Emotion estimation information includes, for example, the tourist's facial expression data, voice data, and behavioral data. The acquisition unit can, for example, use a camera to photograph the tourist's facial expression and acquire facial expression data. The acquisition unit can also record the tourist's voice using a microphone and acquire voice data. Furthermore, the acquisition unit can detect the tourist's behavior with sensors and acquire behavioral data. For example, the acquisition unit detects the tourist's walking pattern and collects it as behavioral data. The emotion estimation unit estimates the tourist's emotions based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit can, for example, use facial expression recognition technology to estimate emotions from the tourist's facial expression data. Furthermore, the emotion estimation unit can also use voice analysis technology to estimate emotions from the tourist's voice data. Furthermore, the emotion estimation unit can also use behavioral analysis technology to estimate emotions from the tourist's behavioral data. For example, the emotion estimation unit takes the tourist's facial expression data as input and estimates their emotions using an AI model that outputs emotions. The decision unit determines a sightseeing plan based on the emotions estimated by the emotion estimation unit. For example, if the tourist is relaxed, the decision unit determines a sightseeing plan that includes relaxing tourist spots. The decision unit can also determine a sightseeing plan that includes exciting tourist spots if the tourist is excited. Furthermore, if the tourist is tired, the decision unit can also determine a sightseeing plan that includes rest. For example, the decision unit takes the tourist's emotion data as input and determines a sightseeing plan using an AI model that outputs sightseeing plans. The suggestion unit proposes the sightseeing plan determined by the decision unit. The suggestion unit can, for example, display the sightseeing plan on a screen. The suggestion unit can also provide the sightseeing plan via voice. Furthermore, the suggestion unit can print and provide the sightseeing plan. For example, the suggestion unit can output the sightseeing plan as text data and display it on a screen. As a result, the sightseeing guidance robot according to this embodiment can improve the quality of the sightseeing experience and increase visitor satisfaction by proposing a sightseeing plan based on the tourist's emotions.

[0078] The data acquisition unit acquires emotion estimation information, which is used to estimate the emotions of tourists. This emotion estimation information includes, for example, facial expression data, voice data, and behavioral data of tourists. For example, the data acquisition unit can use a camera to photograph tourists' facial expressions and acquire facial expression data. Specifically, the camera has high resolution and can capture subtle changes in facial expressions. The data acquisition unit can also use a microphone to record tourists' voices and acquire voice data. The microphone has a noise-canceling function to remove ambient noise, allowing for clear recording of tourists' voices. Furthermore, the data acquisition unit can detect tourists' behavior with sensors and acquire behavioral data. For example, the data acquisition unit can detect tourists' walking patterns and collect them as behavioral data. Walking pattern data is acquired using acceleration sensors and gyroscope sensors and is important information for estimating the tourist's fatigue level and excitement level. This allows the data acquisition unit to efficiently collect information necessary for estimating tourists' emotions from a variety of data sources. Furthermore, the data acquisition unit can process this data in real time and provide it quickly to the emotion estimation unit. This will enable tourist information robots to provide services that respond immediately to the emotions of tourists.

[0079] The emotion estimation unit estimates the tourist's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit estimates emotions from the tourist's facial expression data using facial recognition technology. Specifically, facial recognition technology analyzes facial feature points to identify emotions such as smiling, surprise, and anger. The emotion estimation unit can also estimate emotions from the tourist's voice data using voice analysis technology. Voice analysis technology analyzes the tone, pitch, and speed of the voice to estimate the tourist's emotional state. Furthermore, the emotion estimation unit can also estimate emotions from the tourist's behavioral data using behavioral analysis technology. For example, the emotion estimation unit analyzes the tourist's walking pattern and movement speed to estimate their fatigue level and excitement level. The emotion estimation unit integrates this data and uses an AI model to estimate the tourist's overall emotional state with high accuracy. Specifically, an AI model using deep learning receives facial expression data, voice data, and behavioral data as input and outputs emotions. This AI model has been trained using a large amount of training data and can estimate emotions with high accuracy. This allows the emotion estimation unit to accurately grasp tourists' emotions in real time and provide the information necessary to decide on the next step, which is the sightseeing plan.

[0080] The decision-making unit determines a sightseeing plan based on the emotions estimated by the emotion estimation unit. For example, if the tourist is relaxed, the decision-making unit will determine a sightseeing plan that includes relaxing spots. Specifically, if the tourist is estimated to be relaxed, it will suggest a plan that includes relaxing places such as nature parks, hot springs, and quiet cafes. The decision-making unit can also determine a sightseeing plan that includes exciting spots if the tourist is excited. For example, it will suggest a plan that includes exciting places such as theme parks, adventure sports, and live events. Furthermore, if the tourist is tired, the decision-making unit can determine a sightseeing plan that includes rest areas. For example, if the tourist is estimated to be tired, it will suggest a plan that includes rest areas and relaxation facilities. The decision-making unit determines the sightseeing plan using an AI model that takes the tourist's emotion data as input and outputs a sightseeing plan. This AI model is linked to a database of tourist spots and can select the most suitable spots for the tourist's emotional state. In addition, the decision-making unit can provide a more personalized sightseeing plan by considering the tourist's past behavioral history and preferences. As a result, the decision-making unit can quickly determine the optimal sightseeing plan according to the tourist's emotions and improve the quality of the sightseeing experience.

[0081] The proposal unit proposes the sightseeing plan determined by the decision unit. For example, the proposal unit displays the sightseeing plan on a screen. Specifically, it displays photos, descriptions, and maps of tourist spots on the display of a sightseeing robot, providing visually easy-to-understand guidance. The proposal unit can also provide the sightseeing plan via voice guidance. Voice guidance is user-friendly and conveys information intuitively to tourists. Furthermore, the proposal unit can provide the sightseeing plan in print. For example, the sightseeing plan can be output as text data, printed, and handed to tourists. This allows tourists to carry paper maps and guides, improving convenience. The proposal unit can also customize the method of proposing the sightseeing plan according to the tourist's preferences. For example, it can prioritize voice guidance for visually impaired tourists and emphasize display information for hearing-impaired tourists, providing proposals tailored to individual needs. Furthermore, the proposal unit can collect feedback from tourists and continuously improve the accuracy and effectiveness of the proposals. For example, it can record how tourists reacted to the proposed plan and reflect this in future proposals. This allows the proposal department to provide tourists with optimal sightseeing plans and enhance the quality of their sightseeing experience.

[0082] The tourist information robot is equipped with a reception desk that receives information about tourists' needs or interests. The reception desk receives information about tourists' needs and interests. For example, the reception desk collects information about tourists' needs and interests through questionnaires. The reception desk can also estimate tourists' needs and interests based on their past behavioral history. Furthermore, the reception desk can also estimate tourists' needs and interests by observing their current behavior. For example, the reception desk collects data on places visited and activities participated in by tourists to estimate their needs and interests. This allows the tourist information robot to provide a more personalized tourist experience by proposing sightseeing plans based on tourists' needs and interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input tourist questionnaire data into AI and have the AI ​​perform the estimation of needs and interests.

[0083] The decision unit determines a sightseeing plan based on the emotions estimated by the emotion estimation unit and the information received by the reception unit. For example, if the tourist is relaxed, the decision unit will determine a sightseeing plan that includes relaxing sightseeing spots. The decision unit can also determine a sightseeing plan that includes exciting sightseeing spots if the tourist is excited. Furthermore, if the tourist is tired, the decision unit can also determine a sightseeing plan that includes rest. For example, the decision unit determines a sightseeing plan using an AI model that takes the tourist's emotion data and needs and interests data as input and outputs a sightseeing plan. This allows the sightseeing robot to provide a more appropriate sightseeing plan by determining the sightseeing plan based on the tourist's emotions and needs and interests. Some or all of the above processing in the decision unit may be performed using AI or not. For example, the decision unit can input the tourist's emotion data and needs and interests data into an AI and have the AI ​​perform the sightseeing plan determination.

[0084] The emotion estimation unit estimates the emotions of multiple tourists belonging to a group when the tourists are in a group. For example, the emotion estimation unit collects facial expression data, voice data, and behavioral data from each tourist in the group and estimates the emotion of each tourist. The emotion estimation unit can also estimate the emotion of the entire group based on the emotion data of each tourist. For example, the emotion estimation unit averages the emotion data of each tourist in the group to estimate the emotion of the entire group. This allows the tourist guidance robot to provide a tourist plan suitable for the entire group by estimating the emotions of multiple tourists belonging to the group. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion estimation unit may be performed using AI or not using AI. For example, the emotion estimation unit can input the emotion data of each tourist in the group into the AI ​​and have the AI ​​perform the estimation of the emotion of the entire group.

[0085] The decision unit determines a joint sightseeing plan based on the emotions of multiple tourists estimated by the emotion estimation unit. For example, the decision unit determines a sightseeing plan that includes sightseeing spots that everyone can enjoy, based on the emotional data of each tourist in the group. The decision unit can also determine a sightseeing plan that includes places of common interest, based on the emotional data of each tourist in the group. Furthermore, the decision unit can determine a sightseeing plan that allows everyone to relax, based on the emotional data of each tourist in the group. For example, the decision unit can input the emotional data of each tourist in the group into an AI and have the AI ​​perform the determination of the joint sightseeing plan. In this way, the sightseeing robot improves the overall satisfaction of the group by determining a joint sightseeing plan based on the emotions of multiple tourists. Some or all of the above processing in the decision unit may be performed using AI or not using AI.

[0086] The acquisition unit collects tourist facial expression data, voice data, and behavioral data as information for emotion estimation. For example, the acquisition unit can use a camera to photograph the tourist's facial expression and collect facial expression data. The acquisition unit can also use a microphone to record the tourist's voice and collect voice data. Furthermore, the acquisition unit can detect the tourist's behavior with sensors and collect behavioral data. For example, the acquisition unit can detect the tourist's walking pattern and collect it as behavioral data. This allows the tourist guidance robot to perform more accurate emotion estimation by collecting data on the tourist's facial expression, voice, and behavior. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the tourist's facial expression data into an AI and have the AI ​​perform the collection of emotion estimation information.

[0087] A sightseeing plan includes sightseeing locations, sightseeing routes, and restaurant genres. For example, a sightseeing plan might include recommended tourist spots, efficient sightseeing routes, and restaurant genres offering local specialties. The sightseeing plan is customized based on the tourist's emotions and needs. For instance, if a tourist wants to relax, a plan including relaxing sightseeing spots and restaurants might be suggested. If the tourist wants an active sightseeing experience, a plan including activity-rich sightseeing spots and restaurants could be suggested. Furthermore, if the tourist is seeking a cultural experience, a plan including historical sites and restaurants serving traditional cuisine could be suggested. This allows the sightseeing robot to provide a comprehensive sightseeing experience by including sightseeing locations, sightseeing routes, and restaurant genres in the sightseeing plan. Some or all of the above processes in determining the sightseeing plan may be performed using AI or not. For example, in determining the sightseeing plan, tourist emotion data and data on needs and interests could be input into the AI, allowing the AI ​​to perform the sightseeing plan determination.

[0088] The acquisition unit estimates the tourist's emotions and adjusts the timing of acquiring emotion estimation information based on the estimated emotions. For example, if the tourist is relaxed, the acquisition unit acquires emotion estimation information immediately after they arrive at the tourist site. If the tourist is excited, the acquisition unit can also acquire emotion estimation information immediately after they see the highlights of the tourist site. Furthermore, if the tourist is tired, the acquisition unit can acquire emotion estimation information during breaks or meals. For example, the acquisition unit acquires emotion estimation information at the optimal timing based on the tourist's emotion data. This allows the tourist guidance robot to acquire information at a more appropriate time by adjusting the timing of acquiring emotion estimation information based on the tourist's emotions. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the tourist's emotion data into AI and have the AI ​​adjust the timing of acquiring emotion estimation information.

[0089] The acquisition unit analyzes the tourist's past behavioral history and selects the optimal acquisition method. For example, the acquisition unit selects a method for acquiring sentiment estimation information based on data of places the tourist has visited in the past. The acquisition unit can also analyze the tourist's past travel patterns and acquire sentiment estimation information at the optimal timing. Furthermore, the acquisition unit can select a method for acquiring sentiment estimation information at a specific location based on the tourist's past behavioral history. For example, the acquisition unit can input the tourist's past behavioral data into an AI and have the AI ​​select the optimal acquisition method. This allows the tourist information robot to select the optimal acquisition method by analyzing the tourist's past behavioral history. Some or all of the above processing in the acquisition unit may be performed using AI or not.

[0090] The acquisition unit filters the sentiment estimation information based on the tourist's current activities and areas of interest when acquiring it. For example, if the tourist is actively sightseeing, the acquisition unit acquires sentiment estimation information related to dynamic activities. The acquisition unit can also acquire sentiment estimation information related to cultural interests if the tourist is visiting a cultural site. Furthermore, if the tourist is enjoying shopping, the acquisition unit can acquire sentiment estimation information related to shopping. For example, the acquisition unit can input the tourist's current activities and areas of interest into an AI and have the AI ​​perform the filtering of sentiment estimation information. This allows the tourist information robot to acquire more relevant information by filtering the information based on the tourist's current activities and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI or not.

[0091] The acquisition unit estimates the tourist's emotions and determines the priority of information to acquire based on the estimated emotions. For example, if the tourist is excited, the acquisition unit prioritizes acquiring information related to the factors causing the excitement. The acquisition unit can also prioritize acquiring information related to relaxation if the tourist is relaxed. Furthermore, if the tourist is tired, the acquisition unit can prioritize acquiring information related to rest. For example, the acquisition unit can input the tourist's emotion data into an AI and have the AI ​​determine the priority of information to acquire. This allows the tourist information robot to prioritize acquiring important information by determining the priority of information based on the tourist's emotions. Some or all of the above processing in the acquisition unit may be performed using AI or not.

[0092] The acquisition unit prioritizes acquiring highly relevant information when acquiring information for sentiment estimation, taking into account the tourist's geographical location. For example, if the tourist is in a specific tourist destination, the acquisition unit prioritizes acquiring information related to that destination. The acquisition unit can also prioritize acquiring information related to a specific area if the tourist is in that area. Furthermore, if the tourist is on the move, the acquisition unit can prioritize acquiring information related to the next destination. For example, the acquisition unit can input the tourist's geographical location into an AI and have the AI ​​prioritize acquiring highly relevant information. This allows the tourist information robot to prioritize acquiring highly relevant information by taking the tourist's geographical location into consideration. Some or all of the above processing in the acquisition unit may be performed using AI or not.

[0093] The acquisition unit analyzes the tourist's social media activity and obtains relevant information when acquiring information for sentiment estimation. For example, the acquisition unit analyzes photos and posts shared by the tourist on social media and obtains relevant sentiment estimation information. The acquisition unit can also acquire relevant sentiment estimation information based on information about accounts that the tourist follows on social media. Furthermore, the acquisition unit can also acquire relevant sentiment estimation information based on information about places that the tourist checked in to on social media. For example, the acquisition unit can input the tourist's social media activity into an AI and have the AI ​​acquire the relevant information. This allows the tourist information robot to acquire relevant information by analyzing the tourist's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI or not using AI.

[0094] The emotion estimation unit estimates the tourist's emotions and adjusts the emotion estimation algorithm based on the estimated emotions. For example, if the tourist is relaxed, the emotion estimation unit applies an emotion estimation algorithm related to relaxation. The emotion estimation unit can also apply an emotion estimation algorithm related to excitement if the tourist is excited. Furthermore, if the tourist is tired, the emotion estimation unit can apply an emotion estimation algorithm related to fatigue. For example, the emotion estimation unit can input the tourist's emotion data into an AI and have the AI ​​adjust the emotion estimation algorithm. This allows the tourist guidance robot to improve its estimation accuracy by adjusting the emotion estimation algorithm based on the tourist's emotions. Some or all of the above processing in the emotion estimation unit may be performed using or without AI.

[0095] The emotion estimation unit improves estimation accuracy by referring to the tourist's past emotion data during emotion estimation. For example, the emotion estimation unit estimates the tourist's current emotion based on the tourist's past emotion data. The emotion estimation unit can also analyze the tourist's past emotion patterns to improve estimation accuracy. Furthermore, the emotion estimation unit can adjust the emotion estimation algorithm by referring to the tourist's past emotion data. For example, the emotion estimation unit can input the tourist's past emotion data into an AI and have the AI ​​perform the improvement of estimation accuracy. As a result, the tourist guidance robot improves estimation accuracy by referring to the tourist's past emotion data. Some or all of the above processing in the emotion estimation unit may be performed using AI or not using AI.

[0096] The emotion estimation unit considers the tourist's attribute information (age, gender, etc.) when estimating emotions. For example, the emotion estimation unit adjusts the emotion estimation algorithm based on the tourist's age. It can also adjust the emotion estimation algorithm based on the tourist's gender. Furthermore, the emotion estimation unit can improve the accuracy of emotion estimation based on the tourist's attribute information. For example, the emotion estimation unit can input the tourist's attribute information into an AI and have the AI ​​perform the task of improving the accuracy of emotion estimation. As a result, the tourist information robot improves its estimation accuracy by considering the tourist's attribute information. Some or all of the above processing in the emotion estimation unit may be performed using AI or not.

[0097] The emotion estimation unit estimates the tourist's emotions and adjusts the display method of the estimation results based on the estimated emotions of the tourist. For example, if the tourist is relaxed, the emotion estimation unit provides a display method related to relaxation. The emotion estimation unit can also provide a display method related to excitement if the tourist is excited. Furthermore, if the tourist is tired, the emotion estimation unit can also provide a display method related to fatigue. For example, the emotion estimation unit can input the tourist's emotion data into an AI and have the AI ​​adjust the display method of the estimation results. This allows the tourist information robot to provide more appropriate displays by adjusting the display method of the estimation results based on the tourist's emotions. Some or all of the above processing in the emotion estimation unit may be performed using AI or not using AI.

[0098] The emotion estimation unit considers the geographical distribution of tourists when estimating emotions. For example, if a tourist is in a specific area, the emotion estimation unit applies an emotion estimation algorithm relevant to that area. The emotion estimation unit can also improve the accuracy of emotion estimation based on the geographical distribution of tourists. Furthermore, the emotion estimation unit can adjust the emotion estimation algorithm considering the geographical distribution of tourists. For example, the emotion estimation unit can input the geographical distribution of tourists into an AI and have the AI ​​perform an improvement in the accuracy of emotion estimation. This allows the tourist information robot to improve its estimation accuracy by considering the geographical distribution of tourists. Some or all of the above processing in the emotion estimation unit may be performed using AI or not.

[0099] The sentiment estimation unit improves estimation accuracy by referring to relevant literature on tourists during sentiment estimation. For example, the sentiment estimation unit adjusts the sentiment estimation algorithm based on relevant literature on tourists. The sentiment estimation unit can also improve the accuracy of sentiment estimation by referring to relevant literature on tourists. Furthermore, the sentiment estimation unit can correct the results of sentiment estimation based on relevant literature on tourists. For example, the sentiment estimation unit can input relevant literature on tourists into an AI and have the AI ​​perform the improvement of estimation accuracy. As a result, the tourist information robot improves estimation accuracy by referring to relevant literature on tourists. Some or all of the above processing in the sentiment estimation unit may be performed using AI or not using AI.

[0100] The decision-making unit estimates the tourist's emotions and adjusts the method of determining the sightseeing plan based on the estimated emotions. For example, if the tourist is relaxed, the decision-making unit will prioritize a relaxing sightseeing plan. If the tourist is excited, the decision-making unit may also prioritize a sightseeing plan that will cause excitement. Furthermore, if the tourist is tired, the decision-making unit may also prioritize a sightseeing plan that includes rest. For example, the decision-making unit can input the tourist's emotion data into an AI and have the AI ​​adjust the method of determining the sightseeing plan. This allows the sightseeing robot to provide a more appropriate sightseeing plan by adjusting the method of determining the sightseeing plan based on the tourist's emotions. Some or all of the above processing in the decision-making unit may be performed using AI or not.

[0101] The decision-making unit selects the optimal plan when determining a sightseeing plan by referring to the tourist's past sightseeing history. For example, the decision-making unit selects the optimal sightseeing plan based on the tourist's past sightseeing history. The decision-making unit can also analyze the tourist's past sightseeing patterns and select the optimal sightseeing plan. Furthermore, the decision-making unit can improve the accuracy of sightseeing plan selection by referring to the tourist's past sightseeing history. For example, the decision-making unit can input the tourist's past sightseeing history into an AI and have the AI ​​perform the selection of the optimal sightseeing plan. This allows the sightseeing guidance robot to select the optimal sightseeing plan by referring to the tourist's past sightseeing history. Some or all of the above processing in the decision-making unit may be performed using AI or not using AI.

[0102] The decision-making unit customizes the sightseeing plan based on the tourist's current activities when determining the plan. For example, if the tourist is actively sightseeing, the decision-making unit will customize the sightseeing plan to include dynamic activities. The decision-making unit can also customize the sightseeing plan to relate to cultural interests if the tourist is visiting cultural sites. Furthermore, if the tourist is enjoying shopping, the decision-making unit can also customize the sightseeing plan to relate to shopping. For example, the decision-making unit can input the tourist's current activities into the AI ​​and have the AI ​​perform the customization of the sightseeing plan. This allows the sightseeing robot to provide a more appropriate sightseeing plan by customizing the plan based on the tourist's current activities. Some or all of the above processing in the decision-making unit may be performed using AI or not.

[0103] The decision unit estimates the tourist's emotions and determines the priority of sightseeing plans based on the estimated emotions. For example, if the tourist is relaxed, the decision unit will prioritize sightseeing plans that promote relaxation. The decision unit can also prioritize sightseeing plans that cause excitement if the tourist is excited. Furthermore, if the tourist is tired, the decision unit can prioritize sightseeing plans that include rest. For example, the decision unit can input the tourist's emotion data into an AI and have the AI ​​perform the task of determining the priority of sightseeing plans. This allows the sightseeing robot to prioritize important plans by determining the priority of sightseeing plans based on the tourist's emotions. Some or all of the above processing in the decision unit may be performed using AI or not.

[0104] The decision-making unit selects the optimal plan when determining a sightseeing plan, taking into account the tourist's geographical location. For example, if the tourist is in a specific tourist destination, the decision-making unit will prioritize selecting a sightseeing plan related to that destination. The decision-making unit can also prioritize selecting a sightseeing plan related to a specific area if the tourist is in that area. Furthermore, if the tourist is on the move, the decision-making unit can also prioritize selecting a sightseeing plan related to the next destination. For example, the decision-making unit can input the tourist's geographical location into an AI and have the AI ​​select the optimal sightseeing plan. This allows the sightseeing robot to select the optimal sightseeing plan by taking the tourist's geographical location into consideration. Some or all of the above processing in the decision-making unit may be performed using AI or not.

[0105] The decision-making unit analyzes the tourist's social media activity and proposes a plan when determining a sightseeing plan. For example, the decision-making unit analyzes photos and posts shared by the tourist on social media and proposes a relevant sightseeing plan. The decision-making unit can also propose a relevant sightseeing plan based on information about accounts the tourist follows on social media. Furthermore, the decision-making unit can propose a relevant sightseeing plan based on information about places the tourist has checked in to on social media. For example, the decision-making unit can input the tourist's social media activity into an AI and have the AI ​​propose a sightseeing plan. This allows the sightseeing guidance robot to propose a relevant sightseeing plan by analyzing the tourist's social media activity. Some or all of the above processing in the decision-making unit may be performed using AI or not.

[0106] The suggestion unit estimates the tourist's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the tourist is relaxed, the suggestion unit will present suggestions in a way that promotes relaxation. If the tourist is excited, the suggestion unit can also present suggestions in a way that evokes excitement. Furthermore, if the tourist is tired, the suggestion unit can present suggestions that include the idea of ​​taking a break. For example, the suggestion unit can input the tourist's emotion data into an AI and have the AI ​​adjust the way it presents suggestions. This allows the tourist information robot to make more appropriate suggestions by adjusting the way it presents suggestions based on the tourist's emotions. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0107] The suggestion unit adjusts the level of detail in its suggestions based on the importance of the sightseeing plan. For example, if a tourist spot is important, the suggestion unit will provide a suggestion with detailed information. If a tourist spot is less important, the suggestion unit may provide a suggestion with concise information. Furthermore, the suggestion unit can also adjust the level of detail in its suggestions based on the overall importance of the sightseeing plan. For example, the suggestion unit can input the importance of the sightseeing plan into the AI ​​and have the AI ​​adjust the level of detail in its suggestions. This allows the sightseeing robot to provide more appropriate information by adjusting the level of detail in its suggestions based on the importance of the sightseeing plan. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0108] The suggestion unit applies different suggestion algorithms depending on the category of the tourist plan when making suggestions. For example, in the case of a cultural tourist spot, the suggestion unit applies a suggestion algorithm related to culture. It can also apply a suggestion algorithm related to nature in the case of a natural tourist spot. Furthermore, it can apply a suggestion algorithm related to shopping in the case of a shopping spot. For example, the suggestion unit can input the category of the tourist plan into the AI ​​and have the AI ​​apply the suggestion algorithm. This allows the tourist information robot to make more appropriate suggestions by applying different suggestion algorithms depending on the category of the tourist plan. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0109] The suggestion unit estimates the tourist's emotions and adjusts the length of the suggestions based on the estimated emotions. For example, if the tourist is relaxed, the suggestion unit will provide detailed suggestions. If the tourist is excited, the suggestion unit can also provide concise suggestions. Furthermore, if the tourist is tired, the suggestion unit can provide short suggestions. For example, the suggestion unit can input the tourist's emotion data into an AI and have the AI ​​adjust the length of the suggestions. This allows the tourist guidance robot to provide more appropriate suggestions by adjusting the length of the suggestions based on the tourist's emotions. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0110] The proposal department determines the priority of proposals based on the submission timing of the tourism plan. For example, if the tourism plan is submitted early, the proposal department will prioritize it. Conversely, if the tourism plan is submitted late, the proposal department may postpone it. Furthermore, the proposal department can adjust the priority of proposals based on the submission timing. For example, the proposal department can input the tourism plan submission timing into the AI ​​and have the AI ​​determine the priority of proposals. This allows the tourism guidance robot to make proposals at a more appropriate time by determining the priority of proposals based on the tourism plan submission timing. Some or all of the above processing in the proposal department may be performed using AI or not.

[0111] The suggestion unit adjusts the order of suggestions based on the relevance of the sightseeing plans. For example, the suggestion unit prioritizes suggestions that are highly relevant to the sightseeing plans. It can also postpone suggestions that are less relevant to the sightseeing plans. Furthermore, the suggestion unit can adjust the order of suggestions based on their relevance. For example, the suggestion unit can input the relevance of the sightseeing plans into an AI and have the AI ​​adjust the order of suggestions. This allows the sightseeing guidance robot to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of the sightseeing plans. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0112] The reception desk estimates the tourist's emotions and adjusts the reception method based on the estimated emotions. For example, if the tourist is relaxed, the reception desk provides a relaxing reception method. If the tourist is excited, the reception desk can also provide a reception method that excites them. Furthermore, if the tourist is tired, the reception desk can provide a reception method that includes resting. For example, the reception desk can input the tourist's emotion data into an AI and have the AI ​​adjust the reception method. This allows the tourist information robot to provide more appropriate reception by adjusting the reception method based on the tourist's emotions. Some or all of the above processing in the reception desk may be performed using AI or not.

[0113] The reception desk selects the optimal reception method by referring to data on the tourist's past needs and interests at the time of check-in. For example, the reception desk can select the optimal reception method based on the tourist's past needs. The reception desk can also select the optimal reception method based on the tourist's past interests. Furthermore, the reception desk can adjust the reception method by referring to the tourist's past data. For example, the reception desk can input data on the tourist's past needs and interests into an AI and have the AI ​​select the optimal reception method. This allows the tourist information robot to select the optimal reception method by referring to data on the tourist's past needs and interests. Some or all of the above processing in the reception desk may be performed using AI or not using AI.

[0114] The reception desk estimates the emotions of tourists and determines the priority of services based on the estimated emotions. For example, if a tourist is relaxed, the reception desk will prioritize services that promote relaxation. If a tourist is excited, the reception desk may also prioritize services that would cause excitement. Furthermore, if a tourist is tired, the reception desk may also prioritize services that include rest. For example, the reception desk can input tourist emotion data into an AI and have the AI ​​determine the priority of services. This allows the tourist information robot to provide more appropriate services by prioritizing services based on the emotions of tourists. Some or all of the above processing in the reception desk may be performed using AI or not.

[0115] The reception desk selects the most appropriate reception method at the time of check-in, taking into account the tourist's geographical location. For example, if the tourist is at a specific tourist destination, the reception desk provides a reception method relevant to that destination. The reception desk can also provide a reception method relevant to a specific area if the tourist is in that area. Furthermore, if the tourist is on the move, the reception desk can provide a reception method relevant to their next destination. For example, the reception desk can input the tourist's geographical location into an AI and have the AI ​​select the most appropriate reception method. This allows the tourist information robot to select the most appropriate reception method by taking the tourist's geographical location into account. Some or all of the above processing at the reception desk may be performed using AI or not.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] Tourist information robots can also acquire biometric information from tourists in order to estimate their emotions. For example, they can measure tourists' heart rate and skin electrical activity using sensors and use this data as information for emotion estimation. A high heart rate may indicate excitement or stress, and similarly, high skin electrical activity may indicate tension or excitement. This allows tourist information robots to more accurately estimate emotions based on tourists' biometric information and propose appropriate sightseeing plans. Furthermore, it is possible to monitor tourists' biometric information in real time and dynamically adjust the sightseeing plan in response to changes in emotions. For example, if a tourist suddenly feels tired, the plan can be changed to include a rest. This can further improve the quality of the sightseeing experience.

[0118] The tourist information robot can also analyze tourists' social media activity to estimate their emotions. For example, it can analyze photos and comments posted by tourists on social media to estimate their emotions. If a tourist posts many photos of themselves smiling, it can be estimated that they are experiencing positive emotions. Conversely, if a tourist posts many negative comments, they may be experiencing stress or dissatisfaction. Based on this, the tourist information robot can estimate a tourist's emotions based on their social media activity and suggest an appropriate sightseeing plan. Furthermore, it can also estimate a tourist's interests and needs based on information about the accounts they follow and the groups they participate in. For example, if a tourist follows many museum and art-related accounts, the robot can suggest a sightseeing plan that includes museum visits. This can improve the quality of the sightseeing experience.

[0119] Tourist guidance robots can also analyze tourists' past behavioral history to estimate their emotions. For example, they can collect data on places tourists have visited and activities they have participated in in the past to estimate their emotions. If a tourist has spent a long time at a place they have visited in the past, it can be estimated that they have positive feelings towards that place. Similarly, if a tourist has participated in an activity frequently in the past, it can be estimated that they are interested in that activity. Based on this, tourist guidance robots can estimate emotions based on tourists' past behavioral history and propose appropriate sightseeing plans. Furthermore, it is possible to monitor tourists' past behavioral history in real time and dynamically adjust the sightseeing plan in response to changes in emotions. For example, if a tourist has spent a short time at a place they have visited in the past, the plan can be changed to avoid that place. This can further improve the quality of the sightseeing experience.

[0120] The tourist information robot can also analyze a tourist's music playback history to estimate their emotions. For example, it can analyze the genre and tempo of the music a tourist is playing to estimate their mood. If a tourist is playing relaxing music, it can be estimated that they are relaxed. Conversely, if a tourist is playing upbeat music, it can be estimated that they are excited. This allows the tourist information robot to estimate a tourist's emotions based on their music playback history and suggest an appropriate sightseeing plan. Furthermore, it can monitor the tourist's music playback history in real time and dynamically adjust the sightseeing plan in response to changes in their emotions. For example, if a tourist starts playing relaxing music, the plan can be changed to include relaxing sightseeing spots. This can further improve the quality of the sightseeing experience.

[0121] The tourist information robot can also analyze tourists' sleep data to estimate their emotions. For example, it can analyze the tourist's sleep duration and quality to estimate their emotions. If a tourist has short sleep duration, it can be estimated that they are feeling fatigued or stressed. Similarly, poor sleep quality may also indicate fatigue or stress. Based on this, the tourist information robot can estimate the tourist's emotions based on their sleep data and suggest an appropriate sightseeing plan. Furthermore, it can monitor the tourist's sleep data in real time and dynamically adjust the sightseeing plan in response to changes in their emotions. For example, if a tourist is sleep-deprived, the plan can be changed to include rest. This can further improve the quality of the sightseeing experience.

[0122] The tourist information robot can monitor the congestion levels of tourist destinations in real time based on tourists' interests and needs, and propose sightseeing plans that avoid crowds. For example, if a tourist prefers quiet places, it can suggest less crowded tourist spots. If a tourist wants to visit a popular tourist spot, it can also suggest less crowded times. This allows the tourist information robot to improve the quality of the sightseeing experience by proposing sightseeing plans that take congestion levels into account. Furthermore, it can dynamically adjust sightseeing plans based on real-time congestion monitoring. For example, if a tourist spot suddenly becomes crowded, it can change the destination to a different tourist spot. This allows tourists to enjoy a more comfortable sightseeing experience.

[0123] The tourist information robot can collect local event information in real time based on tourists' interests and needs and incorporate it into their travel plans. For example, if a tourist is interested in music events, it can provide information on local concerts and festivals. If a tourist is interested in food, it can provide information on local food festivals and markets. This allows the tourist information robot to improve the quality of the travel experience by suggesting travel plans that take local events into account. Furthermore, it can collect local event information in real time and dynamically adjust travel plans. For example, if an interesting event is suddenly announced, it can be incorporated into the travel plan. This allows tourists to enjoy local culture and events.

[0124] The tourist information robot can monitor weather information at tourist destinations in real time based on tourists' interests and needs, and propose sightseeing plans that are appropriate for the weather. For example, if tourists prefer outdoor activities, it can suggest outdoor sightseeing spots on days with good weather. Conversely, if tourists prefer indoor activities, it can suggest indoor sightseeing spots on days with bad weather. In this way, the tourist information robot can improve the quality of the sightseeing experience by proposing sightseeing plans that take weather information into account. Furthermore, it can also dynamically adjust sightseeing plans by monitoring weather information in real time. For example, if the weather suddenly deteriorates, it can change to indoor sightseeing spots. This allows tourists to enjoy a comfortable sightseeing experience.

[0125] The tourist information robot can monitor traffic information in real time at tourist destinations based on tourists' interests and needs, and suggest the optimal travel route. For example, if a tourist wants to visit tourist spots efficiently, it can suggest a route that avoids traffic congestion. It can also suggest the best bus or train routes if the tourist wants to use public transportation. This allows the tourist information robot to improve the quality of the tourist experience by suggesting sightseeing plans that take traffic information into account. Furthermore, it can dynamically adjust sightseeing plans by monitoring traffic information in real time. For example, if traffic congestion suddenly occurs, it can change to an alternative route. This allows tourists to visit tourist destinations smoothly.

[0126] The tourist information robot can monitor safety information at tourist destinations in real time based on tourists' interests and needs, and propose safe sightseeing plans. For example, if a tourist prefers safe places, it can suggest tourist spots in areas with low crime rates. Similarly, if a tourist wants to avoid the risk of natural disasters, it can suggest tourist spots in areas with low disaster risk. This allows the tourist information robot to improve the quality of the sightseeing experience by proposing sightseeing plans that take safety information into account. Furthermore, it can dynamically adjust sightseeing plans based on real-time monitoring of safety information. For example, if safety risks suddenly increase, it can switch to a different, safer tourist spot. This allows tourists to enjoy sightseeing with peace of mind.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of tourists. Emotion estimation information includes, for example, the tourist's facial expression data, voice data, and behavioral data. The acquisition unit can, for example, use a camera to photograph the tourist's facial expression and acquire facial expression data. The acquisition unit can also use a microphone to record the tourist's voice and acquire voice data. Furthermore, the acquisition unit can detect the tourist's behavior with sensors and acquire behavioral data. For example, the acquisition unit can detect the tourist's walking pattern and collect it as behavioral data. Step 2: The emotion estimation unit estimates the tourist's emotions based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit can, for example, estimate emotions from the tourist's facial expression data using facial expression recognition technology. The emotion estimation unit can also estimate emotions from the tourist's voice data using voice analysis technology. Furthermore, the emotion estimation unit can also estimate emotions from the tourist's behavior data using behavior analysis technology. For example, the emotion estimation unit can estimate emotions using an AI model that takes the tourist's facial expression data as input and outputs emotions. Step 3: The decision unit determines a sightseeing plan based on the emotions estimated by the emotion estimation unit. For example, if the tourist is relaxed, the decision unit will determine a sightseeing plan that includes relaxing sightseeing spots. The decision unit can also determine a sightseeing plan that includes exciting sightseeing spots if the tourist is excited. Furthermore, if the tourist is tired, the decision unit can also determine a sightseeing plan that includes rest. For example, the decision unit determines a sightseeing plan using an AI model that takes the tourist's emotion data as input and outputs a sightseeing plan. Step 4: The proposal unit proposes the sightseeing plan determined by the decision unit. The proposal unit can, for example, display the sightseeing plan on a screen. The proposal unit can also provide audio guidance on the sightseeing plan. Furthermore, the proposal unit can provide the sightseeing plan in print. For example, the proposal unit can output the sightseeing plan as text data and display it on a screen.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] For example, the acquisition unit is implemented by either the data processing unit 12 or the smart device 14. For example, the emotion estimation unit is implemented by the identification processing unit 290 of the data processing unit 12. For example, the decision unit is implemented by the identification processing unit 290 of the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the smart device 14. For example, the reception unit is implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] For example, the acquisition unit is implemented by either the data processing unit 12 or the smart glasses 214. For example, the emotion estimation unit is implemented by the identification processing unit 290 of the data processing unit 12. For example, the decision unit is implemented by the identification processing unit 290 of the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the smart glasses 214. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] For example, the acquisition unit is implemented by either the data processing unit 12 or the headset terminal 314. For example, the emotion estimation unit is implemented by the identification processing unit 290 of the data processing unit 12. For example, the decision unit is implemented by the identification processing unit 290 of the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the headset terminal 314. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] For example, the acquisition unit is implemented by either the data processing unit 12 or the robot 414. For example, the emotion estimation unit is implemented by the identification processing unit 290 of the data processing unit 12. For example, the decision unit is implemented by the identification processing unit 290 of the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the robot 414. For example, the reception unit is implemented by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) An acquisition unit that acquires emotion estimation information, which is information used to estimate the emotions of tourists, An emotion estimation unit estimates the emotions of the tourist based on the emotion estimation information acquired by the acquisition unit, A decision unit that determines a sightseeing plan based on the emotions estimated by the emotion estimation unit, The system comprises a proposal unit that proposes a sightseeing plan determined by the aforementioned determination unit. A tourist information robot characterized by the following features. (Note 2) It has a reception desk to receive information about the needs or interests of tourists. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned determination unit, Based on the emotions estimated by the emotion estimation unit and the information received by the reception unit, the sightseeing plan is determined. A tourist information robot as described in Appendix 2, characterized by the features described herein. (Note 4) The emotion estimation unit, When tourists are in a group, estimate the emotions of multiple tourists belonging to that group. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned determination unit, The emotion estimation unit determines a joint sightseeing plan based on the emotions of multiple tourists. A tourist information robot as described in Appendix 4, characterized by the features described herein. (Note 6) The acquisition unit is, Facial expression data, voice data, and behavioral data of tourists are collected as information for emotion estimation. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sightseeing plan is Includes tourist attractions, sightseeing routes, and types of restaurants. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system estimates the emotions of tourists and adjusts the timing of acquiring emotion estimation information based on the estimated emotions of the tourists. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, Analyze the past behavioral history of tourists and select the optimal method of acquisition. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring information for sentiment estimation, filtering is performed based on the tourist's current activities and areas of interest. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, The system estimates the sentiments of tourists and prioritizes the information to be acquired based on those estimated sentiments. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring information for sentiment estimation, the system prioritizes acquiring highly relevant information by considering the tourist's geographical location. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, When acquiring information for sentiment estimation, the social media activity of tourists is analyzed to obtain relevant information. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 14) The emotion estimation unit, The system estimates the emotions of tourists and adjusts the emotion estimation algorithm based on the estimated emotions of the tourists. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 15) The emotion estimation unit, When estimating emotions, we improve estimation accuracy by referencing past emotional data of tourists. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 16) The emotion estimation unit, When estimating emotions, the tourist's attribute information is taken into consideration. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 17) The emotion estimation unit, We estimate the sentiments of tourists and adjust how the estimation results are displayed based on those sentiments. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 18) The emotion estimation unit, When estimating emotions, the geographical distribution of tourists is taken into consideration. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 19) The emotion estimation unit, When estimating emotions, we improve estimation accuracy by referring to relevant literature on tourists. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned determination unit, We estimate the emotions of tourists and adjust the decision-making process for tourist plans based on those estimated emotions. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned determination unit, When deciding on a sightseeing plan, the optimal plan is selected by referring to the tourist's past travel history. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned determination unit, When deciding on a sightseeing plan, customize the plan based on the current activity status of the tourists. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned determination unit, The system estimates the sentiments of tourists and prioritizes sightseeing plans based on those estimated sentiments. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned determination unit, When deciding on a sightseeing plan, the optimal plan is selected by taking into account the geographical location information of the tourists. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned determination unit, When deciding on a tourist plan, we analyze tourists' social media activity and propose a plan based on that analysis. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, We estimate the emotions of tourists and adjust the way we present our proposals based on those estimated emotions. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the sightseeing plan. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the sightseeing plan. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, Estimate the sentiment of tourists and adjust the length of the suggestion based on the estimated sentiment. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When submitting proposals, the priority of proposals will be determined based on the timing of the submission of the tourism plan. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the relevance of the sightseeing plan. A tourist information robot as described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reception unit is The system estimates the emotions of tourists and adjusts the check-in process based on those estimated emotions. A tourist information robot as described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned reception unit is At check-in, the system selects the most suitable check-in method by referring to data on the tourist's past needs and interests. A tourist information robot as described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned reception unit is The system estimates the emotions of tourists and determines the priority of service based on those estimated emotions. A tourist information robot as described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned reception unit is At check-in, the most suitable check-in method is selected considering the tourist's geographical location. A tourist information robot as described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires emotion estimation information, which is information used to estimate the emotions of tourists, An emotion estimation unit estimates the emotions of the tourist based on the emotion estimation information acquired by the acquisition unit, A decision unit that determines a sightseeing plan based on the emotions estimated by the emotion estimation unit, The system comprises a proposal unit that proposes a sightseeing plan determined by the aforementioned determination unit. A tourist information robot characterized by the following features.

2. The facility includes a reception area for receiving information on the needs or interests of the aforementioned tourists, The aforementioned determination unit, Based on the emotions estimated by the emotion estimation unit and the information received by the reception unit, the sightseeing plan is determined. The tourist information robot according to feature 2.

3. The emotion estimation unit, When the tourists are a group, the emotions of the multiple tourists belonging to the group are estimated. The emotion estimation unit determines a joint sightseeing plan based on the emotions of multiple tourists. The tourist information robot according to feature 1.

4. The acquisition unit is, The facial expression data, voice data, and behavioral data of the aforementioned tourists are collected as information for emotion estimation. The tourist information robot according to feature 1.

5. The aforementioned sightseeing plan is Includes tourist attractions, sightseeing routes, and types of restaurants. The tourist information robot according to feature 1.

6. The acquisition unit is, The system estimates the emotions of the tourists and adjusts the timing of acquiring the emotion estimation information based on the estimated emotions of the tourists. The tourist information robot according to feature 1.

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