system

Abstract

The system according to the embodiment aims to enable visitors to a large-scale facility such as a stadium to easily find their seats and the location of the facility. [Solution] The system according to the embodiment includes a recognition unit, a guidance unit, a reception unit, and an answering unit. The recognition unit reads the ticket with a camera and recognizes the seat number. The guidance unit guides the passenger to their seat using augmented reality technology based on the seat number identified by the recognition unit. The reception unit accepts questions. The answering unit uses a generation AI to generate an appropriate answer based on the question accepted by the reception unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for spectators to locate their seats or facilities in large facilities such as stadiums.

[0005] The system according to the embodiment aims to enable visitors to a large-scale facility such as a stadium to easily find their seats and the location of the facility. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a guidance unit, a reception unit, and an answering unit. The recognition unit reads the ticket with a camera and recognizes the seat number. The guidance unit guides the passenger to their seat using augmented reality technology based on the seat number identified by the recognition unit. The reception unit accepts questions. The answering unit uses a generation AI to generate an appropriate answer based on the question accepted by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable visitors to easily find their seats and the location of the facility in a large facility such as a stadium. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A spectator support system according to an embodiment of the present invention is a system that utilizes a messaging app (for example, LINE (registered trademark)) and a generation AI to provide a stress-free environment for watching a game. When a visitor does not know where their seat is, this spectator support system reads their ticket with a camera and recognizes the seat number. Next, it uses AR technology to guide them to their seat. At this time, the generation AI recognizes the ticket because the format of the ticket differs depending on the convenience store that issued it. Furthermore, if they are visiting a stadium for the first time and do not know where the facilities are, they can ask a question on the messaging app and the generation AI will answer. For example, when a visitor does not know where their seat is, the ticket is read with a camera. At this time, the ticket is scanned using the camera of a smartphone and the seat number is recognized. For example, when a visitor holds their ticket up to the camera, the generation AI analyzes the ticket format and identifies the seat number. Next, it uses AR technology to guide them to their seat. Based on the seat number identified by the system, the system uses AR technology to display a route to the visitor's seat on their smartphone. For example, arrows and lines are displayed on the smartphone screen, allowing the visitor to follow them to their seat. Furthermore, if it is their first time at a stadium and they are unsure of where facilities are located, they can ask questions via a messaging app and the AI ​​will provide an answer. When a visitor asks a question via a messaging app, such as "Where are the restrooms?" or "Where are the concession stands?", the AI ​​will provide the most appropriate answer based on the stadium's map data. For example, it may provide specific directions such as "The restrooms are 50 meters to the right of your seat." This system allows visitors to enjoy watching the game without stress. Visitors can easily find their seats and quickly locate facilities within the stadium, providing a comfortable viewing environment. This allows the spectator support system to help visitors enjoy watching the game without stress.

[0029] The spectator support system according to the embodiment includes a recognition unit, a guidance unit, a reception unit, and a response unit. The recognition unit reads a ticket with a camera and recognizes the seat number. The recognition unit scans the ticket using, for example, a smartphone camera and recognizes the seat number. For example, when a visitor holds a ticket up to the camera, the recognition unit causes a generation AI to analyze the ticket format and identify the seat number. The recognition unit can also analyze ticket formats that vary depending on the issuing convenience store and identify the seat number. For example, the recognition unit analyzes tickets in different formats, such as PDF, paper, and electronic tickets, and identifies the seat number. The guidance unit uses AR technology to display a route to the visitor's seat on the visitor's smartphone based on the seat number identified by the generation AI. For example, the guidance unit displays arrows or lines on the smartphone screen, allowing the visitor to follow the arrows or lines to navigate to their seat. The guidance unit can also use AR technology to display the route to the visitor's seat on the visitor's smartphone. For example, the guidance unit displays the route using methods such as 3D display, 2D display, and audio guidance. The reception unit accepts questions from attendees via a messaging app. For example, when a visitor asks a question via the messaging app, such as "Where are the restrooms?" or "Where are the concession stands?", the reception unit accepts the question. The reception unit can also accept questions in text format, voice format, or other formats. The answering unit generates an optimal answer based on stadium map data. The answering unit provides specific guidance, such as, "The restrooms are 50 meters to the right of your seat." The answering unit can use an FAQ database, natural language generation technology, or the like to clarify the criteria and generation method for an appropriate answer. This allows the spectator support system according to the embodiment to enable attendees to enjoy watching a game without stress. Some or all of the above-described processing in the recognition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the recognition unit may input image data acquired by scanning a ticket with a camera into the generation AI and cause the generation AI to identify the seat number from the image data. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.For example, the guidance unit uses AR technology to display a route to the visitor's seat on the visitor's smartphone based on the seat number identified by the generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, when the reception unit receives a question from a visitor on a messaging app, it analyzes the question using the generation AI and generates an appropriate answer. Some or all of the above-described processing in the answering unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, when the answering unit generates an optimal answer based on stadium map data, it generates the answer using the generation AI.

[0030] The recognition unit can analyze different ticket formats and identify seat numbers. The recognition unit can analyze tickets in different formats, such as PDF format, paper tickets, and electronic tickets, and identify seat numbers. For example, the recognition unit can analyze a PDF ticket and identify seat numbers. The recognition unit can also scan a paper ticket and convert it into digital data to identify seat numbers. For example, the recognition unit can read a paper ticket with a scanner and identify seat numbers using OCR technology. The recognition unit can also analyze an electronic ticket and identify seat numbers. For example, the recognition unit can analyze electronic ticket data and identify seat numbers. This allows accurate seat numbers to be identified even for tickets in different formats. Some or all of the above-described processing in the recognition unit may be performed using, or without, a generation AI. For example, the recognition unit can input tickets in different formats into the generation AI and have the generation AI identify the seat numbers.

[0031] The guidance unit can use augmented reality technology to display a route to a visitor's seat on the visitor's smartphone. For example, the guidance unit displays arrows or lines on the smartphone screen, allowing the visitor to follow them to their seat. The guidance unit can also display the route using methods such as 3D display, 2D display, and audio guidance. For example, the guidance unit displays the route using 3D display. The guidance unit can also display the route using 2D display. For example, the guidance unit displays the route on a map using 2D display. The guidance unit can also display the route using audio guidance. For example, the guidance unit guides the visitor along the route using audio guidance. This allows the visitor to easily understand the route to their seat. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit uses AR technology to display a route to a visitor's seat on the visitor's smartphone based on the seat number identified by the generation AI.

[0032] The reception unit can accept questions from visitors. For example, when a visitor asks a question such as "Where is the restroom?" or "Where is the shop?" on a messaging app, the reception unit accepts the question. The reception unit can also accept questions in text format, voice format, or other formats. For example, the reception unit accepts questions in text format. The reception unit can also accept questions in voice format. For example, the reception unit accepts questions using voice input. This allows visitors to easily ask questions. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, when the reception unit accepts a visitor's question on a messaging app, the reception unit analyzes the question using a generation AI and generates an appropriate answer.

[0033] The answering unit can generate an appropriate answer based on map data of the stadium. For example, the answering unit provides specific guidance such as, "The restroom is 50 meters to the right of your seat." The answering unit can use an FAQ database, natural language generation technology, etc. to clarify the criteria for and generation method of an appropriate answer. For example, the answering unit generates an appropriate answer using an FAQ database. The answering unit can also generate an appropriate answer using natural language generation technology. For example, the answering unit generates an appropriate answer to a visitor's question using natural language generation technology. This allows visitors to easily understand the location of facilities within the stadium. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, when generating an appropriate answer based on map data of the stadium, the answering unit generates the answer using a generation AI.

[0034] When analyzing the ticket format, the recognition unit can select an appropriate analysis method by referring to past reading history. For example, the recognition unit analyzes the formats of tickets previously read by the generation AI and selects the analysis method with the highest success rate. The recognition unit can also refer to past examples of reading failures and select an analysis method to avoid similar failures. For example, the recognition unit refers to past examples of reading failures and selects an analysis method to avoid similar failures. The recognition unit can also select the optimal analysis method for tickets issued by a specific convenience store from the past reading history. For example, the recognition unit selects the optimal analysis method for tickets issued by a specific convenience store from the past reading history. By referring to the past history, analysis accuracy is improved. Some or all of the above-mentioned processing in the recognition unit may be performed using, or without, the generation AI. For example, the recognition unit can input past reading history into the generation AI and have the generation AI select the optimal analysis method.

[0035] When reading a ticket, the recognition unit can adjust the appropriate reading angle taking into account the visitor's current location information. For example, the generation AI acquires the visitor's location information and calculates the optimal reading angle. The recognition unit can also automatically adjust the camera angle based on the visitor's location information. For example, the recognition unit automatically adjusts the camera angle based on the visitor's location information. The recognition unit can also set the optimal angle to improve reading accuracy by taking into account the visitor's location information. For example, the recognition unit sets the optimal angle to improve reading accuracy by taking into account the visitor's location information. This improves reading accuracy by taking into account the location information. Some or all of the above-described processing in the recognition unit may be performed using, or without, the generation AI. For example, the recognition unit can input the visitor's location information into the generation AI and cause the generation AI to adjust the optimal reading angle.

[0036] When reading a ticket, the recognition unit can select an appropriate reading method depending on the type of device used by the visitor. For example, the generation AI of the recognition unit selects the optimal reading method by taking into account the camera performance of a smartphone. The recognition unit can also select the optimal reading method by taking into account the camera performance of a tablet. For example, the recognition unit selects the optimal reading method by taking into account the camera performance of a tablet. The recognition unit can also select the optimal reading method by taking into account the camera performance of a smartwatch. For example, the recognition unit selects the optimal reading method by taking into account the camera performance of a smartwatch. This enables optimal reading depending on the type of device. Some or all of the above-described processing in the recognition unit may be performed using, or without, the generation AI. For example, the recognition unit can input the type of device used by the visitor into the generation AI and have the generation AI select the optimal reading method.

[0037] When reading a ticket, the recognition unit can improve the appropriate reading accuracy by referring to the visitor's past viewing history. For example, the generation AI analyzes the visitor's past viewing history and selects the optimal reading method. The recognition unit can also improve the reading accuracy for a specific ticket format based on the visitor's past viewing history. For example, the recognition unit improves the reading accuracy for a specific ticket format based on the visitor's past viewing history. The recognition unit can also select an analysis method for improving reading accuracy by referring to the visitor's past viewing history. For example, the recognition unit selects an analysis method for improving reading accuracy by referring to the visitor's past viewing history. In this way, reading accuracy is improved by referring to the past viewing history. Some or all of the above-mentioned processing in the recognition unit may be performed using, or without, the generation AI. For example, the recognition unit can input the visitor's past viewing history into the generation AI and cause the generation AI to improve reading accuracy.

[0038] When displaying a route using augmented reality technology, the guidance unit can select an appropriate route by referring to the visitor's past movement history. For example, the generation AI analyzes the visitor's past movement history and selects the optimal route. The guidance unit can also select a route that avoids congestion based on the visitor's past movement history. For example, the guidance unit selects a route that avoids congestion based on the visitor's past movement history. The guidance unit can also select the most efficient route by referring to the visitor's past movement history. For example, the guidance unit selects the most efficient route by referring to the visitor's past movement history. In this way, the optimal route can be provided by referring to the past movement history. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input the visitor's past movement history into the generation AI and have the generation AI select the optimal route.

[0039] When displaying a route, the guidance unit can adjust an appropriate route taking into account the current congestion situation of visitors. For example, the generation AI of the guidance unit obtains real-time congestion situation and proposes an optimal route. The guidance unit can also propose a detour route to avoid congestion. For example, the guidance unit proposes a detour route to avoid congestion. The guidance unit can also select a route that allows visitors to travel comfortably by taking into account the congestion situation. For example, the guidance unit selects a route that allows visitors to travel comfortably by taking into account the congestion situation. This enables comfortable travel by taking into account the congestion situation. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input real-time congestion situation into the generation AI and have the generation AI adjust the optimal route.

[0040] When displaying a route using augmented reality technology, the guidance unit can adjust the display method appropriately according to the screen size of the visitor's device. For example, the generation AI of the guidance unit provides a display method that matches the screen size of a smartphone. The guidance unit can also provide a display method that matches the screen size of a tablet. For example, the guidance unit provides a display method that matches the screen size of a tablet. The guidance unit can also provide a display method that matches the screen size of a smartwatch. For example, the guidance unit provides a display method that matches the screen size of a smartwatch. This enables optimal display according to the screen size of the device. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input the screen size of the visitor's device to the generation AI and have the generation AI adjust the optimal display method.

[0041] When displaying a route, the guidance unit can improve the accuracy of the appropriate route by referring to the visitor's past viewing history. For example, the generation AI of the guidance unit analyzes the visitor's past viewing history and proposes an optimal route. The guidance unit can also propose a route that avoids crowds based on the visitor's past viewing history. For example, the guidance unit proposes a route that avoids crowds based on the visitor's past viewing history. The guidance unit can also propose the most efficient route by referring to the visitor's past viewing history. For example, the guidance unit proposes the most efficient route by referring to the visitor's past viewing history. In this way, the accuracy of the route is improved by referring to the past viewing history. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input the visitor's past viewing history into the generation AI and cause the generation AI to improve the accuracy of the route.

[0042] When accepting a question, the reception unit can select an appropriate reception method by referring to the visitor's past question history. For example, the generation AI analyzes the visitor's past question history and selects the optimal reception method. The reception unit can also select the optimal answer method for a specific question from the visitor's past question history. For example, the reception unit selects the optimal answer method for a specific question from the visitor's past question history. The reception unit can also select the most efficient reception method by referring to the visitor's past question history. For example, the reception unit selects the most efficient reception method by referring to the visitor's past question history. In this way, the optimal reception method can be selected by referring to the past question history. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the visitor's past question history into the generation AI and have the generation AI select the optimal reception method.

[0043] When accepting a question, the reception unit can provide an appropriate answer by taking into account the visitor's current location information. For example, the reception unit allows a generation AI to acquire the visitor's current location information and provide an optimal answer. The reception unit can also provide information on the nearest facility based on the visitor's location information. For example, the reception unit provides information on the nearest facility based on the visitor's location information. The reception unit can also propose an optimal route by taking into account the visitor's location information. For example, the reception unit proposes an optimal route by taking into account the visitor's location information. In this way, an optimal answer can be provided by taking into account the location information. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the visitor's location information into the generation AI and cause the generation AI to provide an optimal answer.

[0044] When accepting a question, the reception unit can select an appropriate reception method depending on the type of device used by the visitor. For example, the generation AI of the reception unit selects the optimal reception method by taking into account the performance of a smartphone. The reception unit can also select the optimal reception method by taking into account the performance of a tablet. For example, the reception unit selects the optimal reception method by taking into account the performance of a tablet. The reception unit can also select the optimal reception method by taking into account the performance of a smartwatch. For example, the reception unit selects the optimal reception method by taking into account the performance of a smartwatch. This enables optimal reception depending on the type of device. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the type of device used by the visitor into the generation AI and have the generation AI select the optimal reception method.

[0045] When accepting a question, the reception unit can improve the accuracy of appropriate reception by referring to the visitor's past viewing history. For example, the reception unit uses a generation AI to analyze the visitor's past viewing history and select the optimal reception method. The reception unit can also select the optimal answer method for a specific question from the visitor's past viewing history. For example, the reception unit selects the optimal answer method for a specific question from the visitor's past viewing history. The reception unit can also select the most efficient reception method by referring to the visitor's past viewing history. For example, the reception unit selects the most efficient reception method by referring to the visitor's past viewing history. In this way, the reception accuracy is improved by referring to the past viewing history. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the visitor's past viewing history into the generation AI and cause the generation AI to improve the reception accuracy.

[0046] When generating an answer based on stadium map data, the answering unit can select an appropriate answering method by referring to past answering history. For example, the generation AI analyzes past answering history and selects the answering method with the highest success rate. The answering unit can also refer to past examples of failed answers and select an answering method to avoid similar failures. For example, the answering unit refers to past examples of failed answers and selects an answering method to avoid similar failures. The answering unit can also select the optimal answering method for a specific question from the past answering history. For example, the answering unit selects the optimal answering method for a specific question from the past answering history. By referring to the past answering history, the accuracy of the answer is improved. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, the generation AI. For example, the answering unit can input past answering history into the generation AI and have the generation AI select the optimal answering method.

[0047] When generating an answer, the answering unit can adjust the content of the answer appropriately by taking into account the current congestion situation of visitors. For example, the generation AI of the answering unit obtains the real-time congestion situation and provides the optimal answer. The answering unit can also provide facility information to avoid congestion. For example, the answering unit provides facility information to avoid congestion. The answering unit can also provide facility information that visitors can use comfortably by taking into account the congestion situation. For example, the answering unit provides facility information that visitors can use comfortably by taking into account the congestion situation. In this way, the optimal answer can be provided by taking into account the congestion situation. Some or all of the above-mentioned processing in the answering unit may be performed using, or may be performed without using, the generation AI. For example, the answering unit can input the real-time congestion situation into the generation AI and cause the generation AI to adjust the content of the answer optimally.

[0048] When generating an answer based on the stadium map data, the answering unit can adjust the appropriate display method according to the screen size of the visitor's device. For example, the generation AI of the answering unit provides a display method that matches the screen size of a smartphone. The answering unit can also provide a display method that matches the screen size of a tablet. For example, the answering unit provides a display method that matches the screen size of a tablet. The answering unit can also provide a display method that matches the screen size of a smartwatch. For example, the answering unit provides a display method that matches the screen size of a smartwatch. This enables optimal display according to the screen size of the device. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, the generation AI. For example, the answering unit can input the screen size of the visitor's device to the generation AI and have the generation AI adjust the optimal display method.

[0049] When generating an answer, the answering unit can improve the accuracy of the answer by referring to the visitor's past viewing history. For example, the answering unit uses a generation AI to analyze the visitor's past viewing history and provide the optimal answer. The answering unit can also select the optimal answering method for a specific question from the visitor's past viewing history. For example, the answering unit selects the optimal answering method for a specific question from the visitor's past viewing history. The answering unit can also select the most efficient answering method by referring to the visitor's past viewing history. For example, the answering unit selects the most efficient answering method by referring to the visitor's past viewing history. By referring to the past viewing history, the accuracy of the answer is improved. Some or all of the above-described processing in the answering unit may be performed using, or without, the generation AI. For example, the answering unit can input the visitor's past viewing history into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The recognition unit can use facial recognition technology to verify the ticket owner. For example, when a visitor points their face toward the camera, the recognition unit can use facial recognition technology to verify the ticket owner. The recognition unit can also use facial recognition technology to display a warning if the ticket owner does not match. For example, the recognition unit can use facial recognition technology to display a warning if the ticket owner does not match. The recognition unit can also use facial recognition technology to perform normal reading processing if the ticket owner matches. For example, the recognition unit uses facial recognition technology to perform normal reading processing if the ticket owner matches. This can prevent fraudulent use of tickets.

[0052] The guidance unit can detect the walking speed of a visitor and adjust the speed at which guidance is displayed according to the walking speed. For example, if the visitor is walking slowly, the guidance unit can slow down the guidance display speed to make it easier to understand. If the visitor is walking fast, the guidance unit can also speed up the guidance display speed to provide information quickly. For example, if the visitor is walking fast, the guidance unit can speed up the guidance display speed to provide information quickly. The guidance unit can also adjust the content of the guidance display according to the visitor's walking speed. For example, if the visitor is walking slowly, the guidance unit can provide display content that includes detailed information. This makes it possible to display guidance that is optimal for the visitor's walking speed.

[0053] The answering unit can refer to the visitor's past question history and provide a personalized answer. For example, if the visitor has frequently asked about the location of restrooms in the past, the answering unit can provide detailed information about the location of restrooms. For example, if the visitor has asked about the location of a convenience store in the past, the answering unit can also provide information about the location of the convenience store preferentially. For example, if the visitor has asked about the location of a convenience store in the past, the answering unit can provide information about the location of the convenience store preferentially. The answering unit can also provide related information based on the visitor's past question history. For example, if the visitor has asked about the location of a parking lot in the past, the answering unit can provide information about the location of a parking lot. This makes it possible to provide personalized answers based on the visitor's past question history.

[0054] The recognition unit can adjust the ticket reading method taking into account the remaining battery power of the visitor's device. For example, the recognition unit selects a method for rapid reading when the device's battery power is low. The recognition unit can also select a normal reading method when the device's battery power is sufficient. For example, the recognition unit selects a normal reading method when the device's battery power is sufficient. The recognition unit can also perform reading in low power mode when the device's battery power is low. For example, the recognition unit performs reading in low power mode when the device's battery power is low. This enables the optimal reading method to be selected according to the remaining battery power of the device.

[0055] The reception unit can adjust the method for accepting questions by taking into account the visitor's current weather information. For example, if it is raining, the reception unit selects a method for quickly accepting questions so that visitors do not get wet. The reception unit can also select a normal method for accepting questions by taking into account sunny weather. For example, the reception unit selects a normal method for accepting questions by taking into account sunny weather. The reception unit can also provide a method for visitors to comfortably ask questions based on the weather information. For example, the reception unit provides a method for visitors to comfortably ask questions based on the weather information. This makes it possible to optimally accept questions by taking into account weather information.

[0056] The recognition unit can adjust the ticket reading method taking into account the brightness of the visitor's device screen. For example, if the device screen is dark, the recognition unit uses additional lighting to improve reading accuracy. The recognition unit can also select a normal reading method if the device screen is bright. For example, if the device screen is bright, the recognition unit selects a normal reading method. The recognition unit can also adjust the reading method according to the brightness of the device screen. For example, the recognition unit adjusts the reading method according to the brightness of the device screen. This enables the optimal reading method according to the brightness of the device screen.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The recognition unit reads the ticket with a camera and recognizes the seat number. For example, it scans the ticket using a smartphone camera and recognizes the seat number. When the visitor holds the ticket up to the recognition unit's camera, the generation AI analyzes the ticket format and identifies the seat number. The recognition unit can also analyze ticket formats, which vary depending on the convenience store where they were issued, and identify the seat number. For example, it can analyze tickets in different formats, such as PDF, paper, and electronic tickets, and identify the seat number. Step 2: Based on the seat number identified by the recognition unit, the guidance unit uses AR technology to display the route to the visitor's seat on the visitor's smartphone. For example, arrows and lines are displayed on the smartphone screen, allowing the visitor to follow them to their seat. The guidance unit can also display the route in 3D, 2D, or by voice guidance. Step 3: The reception desk accepts questions from visitors via the messaging app. For example, if a visitor asks a question such as "Where are the restrooms?" or "Where is the shop?" via the messaging app, the question is accepted. The reception desk can also accept questions in text format, voice format, or other formats. Step 4: In the answering section, the generation AI generates an appropriate answer based on the question received by the reception section. For example, it could provide specific directions such as "The toilets are 50 meters to the right of your seat" based on stadium map data. The answering section can use FAQ databases, natural language generation technology, etc. to clarify the criteria for appropriate answers and the generation method.

[0059] (Example 2) A spectator support system according to an embodiment of the present invention utilizes a messaging app (e.g., LINE) and a generation AI to provide a stress-free viewing environment. When a visitor is unsure of where their seat is, this spectator support system scans their ticket with a camera and recognizes the seat number. Next, AR technology is used to guide them to their seat. Because ticket formats vary depending on the convenience store where the ticket was issued, the generation AI recognizes the ticket. Furthermore, if a visitor is unsure of the location of facilities at a stadium they are visiting for the first time, they can ask questions via a messaging app, and the generation AI will provide an answer. For example, if a visitor does not know where their seat is, the system scans their ticket with a camera. The ticket is scanned using a smartphone camera to recognize the seat number. For example, when a visitor holds their ticket up to the camera, the generation AI analyzes the ticket format and identifies the seat number. Next, AR technology is used to guide the visitor to their seat. Based on the seat number identified by the generation AI, AR technology is used to display a route to their seat on the visitor's smartphone. For example, arrows and lines are displayed on the smartphone screen, allowing the visitor to follow the directions to their seat. Furthermore, if a visitor is visiting a stadium for the first time and does not know where the facilities are, they can ask a question via a messaging app and the AI ​​will provide an answer. When a visitor asks a question via a messaging app such as "Where are the restrooms?" or "Where are the concession stands?", the AI ​​will provide the most appropriate answer based on the stadium's map data. For example, it will provide specific directions such as "The restrooms are 50 meters to the right of your seat." This system allows visitors to enjoy watching the game without stress. They can easily find their seats and quickly understand the locations of facilities within the stadium, providing a comfortable viewing environment. This allows the spectator support system to allow visitors to enjoy watching the game without stress.

[0060] The spectator support system according to the embodiment includes a recognition unit, a guidance unit, a reception unit, and a response unit. The recognition unit reads a ticket with a camera and recognizes the seat number. The recognition unit scans the ticket using, for example, a smartphone camera and recognizes the seat number. For example, when a visitor holds a ticket up to the camera, the recognition unit causes a generation AI to analyze the ticket format and identify the seat number. The recognition unit can also analyze ticket formats that vary depending on the issuing convenience store and identify the seat number. For example, the recognition unit analyzes tickets in different formats, such as PDF, paper, and electronic tickets, and identifies the seat number. The guidance unit uses AR technology to display a route to the visitor's seat on the visitor's smartphone based on the seat number identified by the generation AI. For example, the guidance unit displays arrows or lines on the smartphone screen, allowing the visitor to follow the arrows or lines to navigate to their seat. The guidance unit can also use AR technology to display the route to the visitor's seat on the visitor's smartphone. For example, the guidance unit displays the route using methods such as 3D display, 2D display, and audio guidance. The reception unit accepts questions from attendees via a messaging app. For example, when a visitor asks a question via the messaging app, such as "Where are the restrooms?" or "Where are the concession stands?", the reception unit accepts the question. The reception unit can also accept questions in text format, voice format, or other formats. The answering unit generates an optimal answer based on stadium map data. The answering unit provides specific guidance, such as, "The restrooms are 50 meters to the right of your seat." The answering unit can use an FAQ database, natural language generation technology, or the like to clarify the criteria and generation method for an appropriate answer. This allows the spectator support system according to the embodiment to enable attendees to enjoy watching a game without stress. Some or all of the above-described processing in the recognition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the recognition unit may input image data acquired by scanning a ticket with a camera into the generation AI and cause the generation AI to identify the seat number from the image data. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.For example, the guidance unit uses AR technology to display a route to the visitor's seat on the visitor's smartphone based on the seat number identified by the generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, when the reception unit receives a question from a visitor on a messaging app, it analyzes the question using the generation AI and generates an appropriate answer. Some or all of the above-described processing in the answering unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, when the answering unit generates an optimal answer based on stadium map data, it generates the answer using the generation AI.

[0061] The recognition unit can analyze different ticket formats and identify seat numbers. The recognition unit can analyze tickets in different formats, such as PDF format, paper tickets, and electronic tickets, and identify seat numbers. For example, the recognition unit can analyze a PDF ticket and identify seat numbers. The recognition unit can also scan a paper ticket and convert it into digital data to identify seat numbers. For example, the recognition unit can read a paper ticket with a scanner and identify seat numbers using OCR technology. The recognition unit can also analyze an electronic ticket and identify seat numbers. For example, the recognition unit can analyze electronic ticket data and identify seat numbers. This allows accurate seat numbers to be identified even for tickets in different formats. Some or all of the above-described processing in the recognition unit may be performed using, or without, a generation AI. For example, the recognition unit can input tickets in different formats into the generation AI and have the generation AI identify the seat numbers.

[0062] The guidance unit can use augmented reality technology to display a route to a visitor's seat on the visitor's smartphone. For example, the guidance unit displays arrows or lines on the smartphone screen, allowing the visitor to follow them to their seat. The guidance unit can also display the route using methods such as 3D display, 2D display, and audio guidance. For example, the guidance unit displays the route using 3D display. The guidance unit can also display the route using 2D display. For example, the guidance unit displays the route on a map using 2D display. The guidance unit can also display the route using audio guidance. For example, the guidance unit guides the visitor along the route using audio guidance. This allows the visitor to easily understand the route to their seat. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit uses AR technology to display a route to a visitor's seat on the visitor's smartphone based on the seat number identified by the generation AI.

[0063] The reception unit can accept questions from visitors. For example, when a visitor asks a question such as "Where is the restroom?" or "Where is the shop?" on a messaging app, the reception unit accepts the question. The reception unit can also accept questions in text format, voice format, or other formats. For example, the reception unit accepts questions in text format. The reception unit can also accept questions in voice format. For example, the reception unit accepts questions using voice input. This allows visitors to easily ask questions. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, when the reception unit accepts a visitor's question on a messaging app, the reception unit analyzes the question using a generation AI and generates an appropriate answer.

[0064] The answering unit can generate an appropriate answer based on map data of the stadium. For example, the answering unit provides specific guidance such as, "The restroom is 50 meters to the right of your seat." The answering unit can use an FAQ database, natural language generation technology, etc. to clarify the criteria for and generation method of an appropriate answer. For example, the answering unit generates an appropriate answer using an FAQ database. The answering unit can also generate an appropriate answer using natural language generation technology. For example, the answering unit generates an appropriate answer to a visitor's question using natural language generation technology. This allows visitors to easily understand the location of facilities within the stadium. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, when generating an appropriate answer based on map data of the stadium, the answering unit generates the answer using a generation AI.

[0065] The recognition unit can estimate the emotion of a visitor and adjust the accuracy of reading the ticket based on the estimated emotion. For example, if the visitor is nervous, the recognition unit can increase the accuracy of reading the ticket with the generation AI to prevent misrecognition. The recognition unit can also cause the generation AI to recognize the ticket with normal reading accuracy if the visitor is relaxed. For example, if the visitor is relaxed, the recognition unit can recognize the ticket with normal reading accuracy. If the visitor is in a hurry, the recognition unit can also cause the generation AI to prioritize reading speed and quickly identify the seat number. For example, if the visitor is in a hurry, the recognition unit can prioritize reading speed and quickly identify the seat number. This adjusts the reading accuracy according to the visitor's emotion, preventing misrecognition. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit can be performed using, for example, the generation AI, or without the generation AI. For example, the recognition unit inputs the visitor's emotions into the generation AI, and the ticket's price is calculated based on the emotions. The generation AI can be made to perform a process to adjust the reading accuracy.

[0066] When analyzing the ticket format, the recognition unit can select an appropriate analysis method by referring to past reading history. For example, the recognition unit analyzes the formats of tickets previously read by the generation AI and selects the analysis method with the highest success rate. The recognition unit can also refer to past examples of reading failures and select an analysis method to avoid similar failures. For example, the recognition unit refers to past examples of reading failures and selects an analysis method to avoid similar failures. The recognition unit can also select the optimal analysis method for tickets issued by a specific convenience store from the past reading history. For example, the recognition unit selects the optimal analysis method for tickets issued by a specific convenience store from the past reading history. By referring to the past history, analysis accuracy is improved. Some or all of the above-mentioned processing in the recognition unit may be performed using, or without, the generation AI. For example, the recognition unit can input past reading history into the generation AI and have the generation AI select the optimal analysis method.

[0067] When reading a ticket, the recognition unit can adjust the appropriate reading angle taking into account the visitor's current location information. For example, the generation AI acquires the visitor's location information and calculates the optimal reading angle. The recognition unit can also automatically adjust the camera angle based on the visitor's location information. For example, the recognition unit automatically adjusts the camera angle based on the visitor's location information. The recognition unit can also set the optimal angle to improve reading accuracy by taking into account the visitor's location information. For example, the recognition unit sets the optimal angle to improve reading accuracy by taking into account the visitor's location information. This improves reading accuracy by taking into account the location information. Some or all of the above-described processing in the recognition unit may be performed using, or without, the generation AI. For example, the recognition unit can input the visitor's location information into the generation AI and cause the generation AI to adjust the optimal reading angle.

[0068] The recognition unit can estimate the emotion of the attendee and adjust the ticket reading speed based on the estimated emotion. For example, if the attendee is nervous, the recognition unit causes the generation AI to slow down the reading speed and accurately recognize the seat number. The recognition unit can also cause the generation AI to recognize the ticket at a normal reading speed if the attendee is relaxed. For example, if the attendee is relaxed, the recognition unit can recognize the ticket at a normal reading speed. If the attendee is in a hurry, the recognition unit can also cause the generation AI to increase the reading speed and quickly identify the seat number. For example, if the attendee is in a hurry, the recognition unit can increase the reading speed and quickly identify the seat number. This allows for fast and accurate reading by adjusting the reading speed according to the attendee's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recognition unit can be performed, for example, using the generation AI, or without the generation AI. For example, the recognition unit can input the visitor's emotions into the generation AI and have the generation AI perform a process to adjust the ticket reading speed based on the emotions.

[0069] When reading a ticket, the recognition unit can select an appropriate reading method depending on the type of device used by the visitor. For example, the generation AI of the recognition unit selects the optimal reading method by taking into account the camera performance of a smartphone. The recognition unit can also select the optimal reading method by taking into account the camera performance of a tablet. For example, the recognition unit selects the optimal reading method by taking into account the camera performance of a tablet. The recognition unit can also select the optimal reading method by taking into account the camera performance of a smartwatch. For example, the recognition unit selects the optimal reading method by taking into account the camera performance of a smartwatch. This enables optimal reading depending on the type of device. Some or all of the above-described processing in the recognition unit may be performed using, or without, the generation AI. For example, the recognition unit can input the type of device used by the visitor into the generation AI and have the generation AI select the optimal reading method.

[0070] When reading a ticket, the recognition unit can improve the appropriate reading accuracy by referring to the visitor's past viewing history. For example, the generation AI analyzes the visitor's past viewing history and selects the optimal reading method. The recognition unit can also improve the reading accuracy for a specific ticket format based on the visitor's past viewing history. For example, the recognition unit improves the reading accuracy for a specific ticket format based on the visitor's past viewing history. The recognition unit can also select an analysis method for improving reading accuracy by referring to the visitor's past viewing history. For example, the recognition unit selects an analysis method for improving reading accuracy by referring to the visitor's past viewing history. In this way, reading accuracy is improved by referring to the past viewing history. Some or all of the above-mentioned processing in the recognition unit may be performed using, or without, the generation AI. For example, the recognition unit can input the visitor's past viewing history into the generation AI and cause the generation AI to improve reading accuracy.

[0071] The guidance unit can estimate the visitor's emotions and adjust the guidance display method based on the estimated visitor's emotions. For example, if the visitor is nervous, the guidance unit provides a simple, highly visible display method. If the visitor is relaxed, the guidance unit can also provide a display method including detailed information. For example, if the visitor is relaxed, the guidance unit provides a display method including detailed information. If the visitor is in a hurry, the guidance unit can also provide a display method that focuses on the main points. For example, if the visitor is in a hurry, the guidance unit provides a display method that focuses on the main points. This enables optimal guidance display according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the guidance unit can be performed using, for example, the generation AI, or without the generation AI. For example, the guidance section can input the emotions of visitors into the generation AI and have the generation AI execute a process to adjust the way the guidance is displayed based on the emotions.

[0072] When displaying a route using augmented reality technology, the guidance unit can select an appropriate route by referring to the visitor's past movement history. For example, the generation AI analyzes the visitor's past movement history and selects the optimal route. The guidance unit can also select a route that avoids congestion based on the visitor's past movement history. For example, the guidance unit selects a route that avoids congestion based on the visitor's past movement history. The guidance unit can also select the most efficient route by referring to the visitor's past movement history. For example, the guidance unit selects the most efficient route by referring to the visitor's past movement history. In this way, the optimal route can be provided by referring to the past movement history. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input the visitor's past movement history into the generation AI and have the generation AI select the optimal route.

[0073] When displaying a route, the guidance unit can adjust an appropriate route taking into account the current congestion situation of visitors. For example, the generation AI of the guidance unit obtains real-time congestion situation and proposes an optimal route. The guidance unit can also propose a detour route to avoid congestion. For example, the guidance unit proposes a detour route to avoid congestion. The guidance unit can also select a route that allows visitors to travel comfortably by taking into account the congestion situation. For example, the guidance unit selects a route that allows visitors to travel comfortably by taking into account the congestion situation. This enables comfortable travel by taking into account the congestion situation. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input real-time congestion situation into the generation AI and have the generation AI adjust the optimal route.

[0074] The guidance unit can estimate the emotions of the visitor and adjust the speed at which the guidance is displayed based on the estimated emotions of the visitor. For example, if the visitor is nervous, the guidance unit slows down the speed at which the guidance is displayed to make it easier to understand. If the visitor is relaxed, the guidance unit can also provide guidance at a normal display speed. For example, if the visitor is relaxed, the guidance unit can provide guidance at a normal display speed. If the visitor is in a hurry, the guidance unit can also speed up the speed at which the guidance is displayed to provide information quickly. For example, if the visitor is in a hurry, the guidance unit can speed up the speed at which the guidance is displayed to provide information quickly. This allows the information to be displayed in accordance with the emotions of the visitor. This allows for an optimal display speed based on the emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit may input the emotion of the visitor into the generation AI and have the generation AI execute processing to adjust the guidance display speed based on the emotion.

[0075] When displaying a route using augmented reality technology, the guidance unit can adjust the display method appropriately according to the screen size of the visitor's device. For example, the generation AI of the guidance unit provides a display method that matches the screen size of a smartphone. The guidance unit can also provide a display method that matches the screen size of a tablet. For example, the guidance unit provides a display method that matches the screen size of a tablet. The guidance unit can also provide a display method that matches the screen size of a smartwatch. For example, the guidance unit provides a display method that matches the screen size of a smartwatch. This enables optimal display according to the screen size of the device. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input the screen size of the visitor's device to the generation AI and have the generation AI adjust the optimal display method.

[0076] When displaying a route, the guidance unit can improve the accuracy of the appropriate route by referring to the visitor's past viewing history. For example, the generation AI of the guidance unit analyzes the visitor's past viewing history and proposes an optimal route. The guidance unit can also propose a route that avoids crowds based on the visitor's past viewing history. For example, the guidance unit proposes a route that avoids crowds based on the visitor's past viewing history. The guidance unit can also propose the most efficient route by referring to the visitor's past viewing history. For example, the guidance unit proposes the most efficient route by referring to the visitor's past viewing history. In this way, the accuracy of the route is improved by referring to the past viewing history. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input the visitor's past viewing history into the generation AI and cause the generation AI to improve the accuracy of the route.

[0077] The reception unit can estimate the visitor's emotions and adjust the method for accepting questions based on the estimated visitor's emotions. For example, if the visitor is nervous, the reception unit can provide a simple interface and minimize input steps. If the visitor is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the visitor is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the visitor is in a hurry, the reception unit can prioritize voice input and quickly accept questions. For example, if the visitor is in a hurry, the reception unit can prioritize voice input and quickly accept questions. This enables optimal question acceptance according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the emotions of visitors into the generation AI and have the generation AI execute a process to adjust the way questions are received based on the emotions.

[0078] When accepting a question, the reception unit can select an appropriate reception method by referring to the visitor's past question history. For example, the generation AI analyzes the visitor's past question history and selects the optimal reception method. The reception unit can also select the optimal answer method for a specific question from the visitor's past question history. For example, the reception unit selects the optimal answer method for a specific question from the visitor's past question history. The reception unit can also select the most efficient reception method by referring to the visitor's past question history. For example, the reception unit selects the most efficient reception method by referring to the visitor's past question history. In this way, the optimal reception method can be selected by referring to the past question history. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the visitor's past question history into the generation AI and have the generation AI select the optimal reception method.

[0079] When accepting a question, the reception unit can provide an appropriate answer by taking into account the visitor's current location information. For example, the reception unit allows a generation AI to acquire the visitor's current location information and provide an optimal answer. The reception unit can also provide information on the nearest facility based on the visitor's location information. For example, the reception unit provides information on the nearest facility based on the visitor's location information. The reception unit can also propose an optimal route by taking into account the visitor's location information. For example, the reception unit proposes an optimal route by taking into account the visitor's location information. In this way, an optimal answer can be provided by taking into account the location information. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the visitor's location information into the generation AI and cause the generation AI to provide an optimal answer.

[0080] The reception unit can estimate the visitor's emotions and adjust the speed at which questions are accepted based on the estimated visitor's emotions. For example, if the visitor is nervous, the reception unit can slow down the acceptance speed and accept questions accurately. If the visitor is relaxed, the reception unit can also accept questions at a normal acceptance speed. For example, if the visitor is relaxed, the reception unit can accept questions at a normal acceptance speed. If the visitor is in a hurry, the reception unit can also speed up the acceptance speed and accept questions quickly. For example, if the visitor is in a hurry, the reception unit can speed up the acceptance speed and accept questions quickly. This enables the optimal acceptance speed according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the emotions of visitors into the generation AI and have the generation AI execute a process to adjust the speed at which questions are accepted based on the emotions.

[0081] When accepting a question, the reception unit can select an appropriate reception method depending on the type of device used by the visitor. For example, the generation AI of the reception unit selects the optimal reception method by taking into account the performance of a smartphone. The reception unit can also select the optimal reception method by taking into account the performance of a tablet. For example, the reception unit selects the optimal reception method by taking into account the performance of a tablet. The reception unit can also select the optimal reception method by taking into account the performance of a smartwatch. For example, the reception unit selects the optimal reception method by taking into account the performance of a smartwatch. This enables optimal reception depending on the type of device. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the type of device used by the visitor into the generation AI and have the generation AI select the optimal reception method.

[0082] When accepting a question, the reception unit can improve the accuracy of appropriate reception by referring to the visitor's past viewing history. For example, the reception unit uses a generation AI to analyze the visitor's past viewing history and select the optimal reception method. The reception unit can also select the optimal answer method for a specific question from the visitor's past viewing history. For example, the reception unit selects the optimal answer method for a specific question from the visitor's past viewing history. The reception unit can also select the most efficient reception method by referring to the visitor's past viewing history. For example, the reception unit selects the most efficient reception method by referring to the visitor's past viewing history. In this way, the reception accuracy is improved by referring to the past viewing history. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the visitor's past viewing history into the generation AI and cause the generation AI to improve the reception accuracy.

[0083] The answering section can estimate the visitor's emotions and adjust the way the answer is expressed based on the estimated visitor's emotions. For example, if the visitor is nervous, the answering section uses simple and easy-to-understand expressions. If the visitor is relaxed, the answering section can also use expressions that include detailed information. For example, if the visitor is relaxed, the answering section can use expressions that include detailed information. If the visitor is in a hurry, the answering section can also use concise expressions that get to the point. For example, if the visitor is in a hurry, the answering section uses concise expressions that get to the point. This makes it possible to adjust the way the answer is expressed based on the visitor's emotions. This makes it possible to express an optimal answer according to the emotion. Emotion estimation is realized, for example, by using an emotion estimation function using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the answering unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the answering unit may input the visitor's emotion into the generation AI and cause the generation AI to perform processing to adjust the way the answer is expressed based on the emotion.

[0084] When generating an answer based on stadium map data, the answering unit can select an appropriate answering method by referring to past answering history. For example, the generation AI analyzes past answering history and selects the answering method with the highest success rate. The answering unit can also refer to past examples of failed answers and select an answering method to avoid similar failures. For example, the answering unit refers to past examples of failed answers and selects an answering method to avoid similar failures. The answering unit can also select the optimal answering method for a specific question from the past answering history. For example, the answering unit selects the optimal answering method for a specific question from the past answering history. By referring to the past answering history, the accuracy of the answer is improved. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, the generation AI. For example, the answering unit can input past answering history into the generation AI and have the generation AI select the optimal answering method.

[0085] When generating an answer, the answering unit can adjust the content of the answer appropriately by taking into account the current congestion situation of visitors. For example, the generation AI of the answering unit obtains the real-time congestion situation and provides the optimal answer. The answering unit can also provide facility information to avoid congestion. For example, the answering unit provides facility information to avoid congestion. The answering unit can also provide facility information that visitors can use comfortably by taking into account the congestion situation. For example, the answering unit provides facility information that visitors can use comfortably by taking into account the congestion situation. In this way, the optimal answer can be provided by taking into account the congestion situation. Some or all of the above-mentioned processing in the answering unit may be performed using, or may be performed without using, the generation AI. For example, the answering unit can input the real-time congestion situation into the generation AI and cause the generation AI to adjust the content of the answer optimally.

[0086] The answering unit can estimate the visitor's emotions and adjust the display speed of the answers based on the estimated visitor's emotions. For example, if the visitor is nervous, the answering unit can slow down the display speed of the answers to make them easier to understand. If the visitor is relaxed, the answering unit can also provide the answers at a normal display speed. For example, if the visitor is relaxed, the answering unit can provide the answers at a normal display speed. If the visitor is in a hurry, the answering unit can also speed up the display speed of the answers to provide information quickly. For example, if the visitor is in a hurry, the answering unit can speed up the display speed of the answers to provide information quickly. This enables the optimal display speed according to the visitor's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the answering unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the response unit can input the visitor's emotions into the generation AI and cause the generation AI to perform a process of adjusting the speed at which the answers are displayed based on the emotions.

[0087] When generating an answer based on the stadium map data, the answering unit can adjust the appropriate display method according to the screen size of the visitor's device. For example, the generation AI of the answering unit provides a display method that matches the screen size of a smartphone. The answering unit can also provide a display method that matches the screen size of a tablet. For example, the answering unit provides a display method that matches the screen size of a tablet. The answering unit can also provide a display method that matches the screen size of a smartwatch. For example, the answering unit provides a display method that matches the screen size of a smartwatch. This enables optimal display according to the screen size of the device. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, the generation AI. For example, the answering unit can input the screen size of the visitor's device to the generation AI and have the generation AI adjust the optimal display method.

[0088] When generating an answer, the answering unit can improve the accuracy of the answer by referring to the visitor's past viewing history. For example, the answering unit uses a generation AI to analyze the visitor's past viewing history and provide the optimal answer. The answering unit can also select the optimal answering method for a specific question from the visitor's past viewing history. For example, the answering unit selects the optimal answering method for a specific question from the visitor's past viewing history. The answering unit can also select the most efficient answering method by referring to the visitor's past viewing history. For example, the answering unit selects the most efficient answering method by referring to the visitor's past viewing history. By referring to the past viewing history, the accuracy of the answer is improved. Some or all of the above-described processing in the answering unit may be performed using, or without, the generation AI. For example, the answering unit can input the visitor's past viewing history into the generation AI and cause the generation AI to improve the accuracy of the answer. === Hard Collateral 1-1 === Each of the multiple elements including the recognition unit, guidance unit, reception unit, and answering unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit reads a ticket using the camera 42 of the smart device 14, and identifies the seat number using the identification processing unit 290 of the data processing device 12. The guidance unit displays a route to the user's seat using AR technology using the control unit 46A of the smart device 14. The reception unit receives a question on a message app via the communication I / F 44 of the smart device 14, for example. The answering unit generates an optimal answer based on stadium map data using the identification processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned recognition unit, guidance unit, reception unit, and answering unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit reads a ticket using the camera 42 of the smart glasses 214 and identifies the seat number using the identification processing unit 290 of the data processing device 12. The guidance unit displays a route to the user's seat using AR technology using the control unit 46A of the smart glasses 214. The reception unit receives a question on a message app via the communication I / F 44 of the smart glasses 214, for example. The answering unit generates an optimal answer based on stadium map data using the identification processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-3 === Each of the multiple elements including the above-described recognition unit, guidance unit, reception unit, and answering unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the recognition unit reads a ticket using the camera 42 of the headset terminal 314, and identifies the seat number using the identification processing unit 290 of the data processing device 12. For example, the guidance unit displays a route to the user's seat using AR technology using the control unit 46A of the headset terminal 314. For example, the reception unit receives a question on a message app via the communication I / F 44 of the headset terminal 314. For example, the answering unit generates an optimal answer based on stadium map data using the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned recognition unit, guidance unit, reception unit, and answering unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit reads a ticket using the camera 42 of the robot 414, and identifies the seat number using the identification processing unit 290 of the data processing device 12. For example, the guidance unit displays a route to the user's seat using AR technology using the control unit 46A of the robot 414. For example, the reception unit receives a question on a message app via the communication I / F 44 of the robot 414. For example, the answering unit generates an optimal answer based on stadium map data using the identification processing unit 290 of the data processing device 12.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The recognition unit can use facial recognition technology to verify the ticket owner. For example, when a visitor points their face toward the camera, the recognition unit can use facial recognition technology to verify the ticket owner. The recognition unit can also use facial recognition technology to display a warning if the ticket owner does not match. For example, the recognition unit can use facial recognition technology to display a warning if the ticket owner does not match. The recognition unit can also use facial recognition technology to perform normal reading processing if the ticket owner matches. For example, the recognition unit uses facial recognition technology to perform normal reading processing if the ticket owner matches. This can prevent fraudulent use of tickets.

[0091] The guidance unit can detect the walking speed of a visitor and adjust the speed at which guidance is displayed according to the walking speed. For example, if the visitor is walking slowly, the guidance unit can slow down the guidance display speed to make it easier to understand. If the visitor is walking fast, the guidance unit can also speed up the guidance display speed to provide information quickly. For example, if the visitor is walking fast, the guidance unit can speed up the guidance display speed to provide information quickly. The guidance unit can also adjust the content of the guidance display according to the visitor's walking speed. For example, if the visitor is walking slowly, the guidance unit can provide display content that includes detailed information. This makes it possible to display guidance that is optimal for the visitor's walking speed.

[0092] The reception unit can analyze the visitor's voice tone, infer their emotions, and adjust the way questions are accepted. For example, if the visitor's voice tone is high, the reception unit can infer that they are nervous and provide a simple interface. If the visitor's voice tone is low, the reception unit can infer that they are relaxed and provide more detailed input options. For example, if the visitor's voice tone is low, the reception unit can provide more detailed input options. If the visitor's voice tone sounds urgent, the reception unit can also prioritize voice input for quickly accepting questions. For example, if the visitor's voice tone sounds urgent, the reception unit prioritizes voice input for quickly accepting questions. This makes it possible to accept questions optimally according to the visitor's voice tone.

[0093] The answering unit can refer to the visitor's past question history and provide a personalized answer. For example, if the visitor has frequently asked about the location of restrooms in the past, the answering unit can provide detailed information about the location of restrooms. For example, if the visitor has asked about the location of a convenience store in the past, the answering unit can also provide information about the location of the convenience store preferentially. For example, if the visitor has asked about the location of a convenience store in the past, the answering unit can provide information about the location of the convenience store preferentially. The answering unit can also provide related information based on the visitor's past question history. For example, if the visitor has asked about the location of a parking lot in the past, the answering unit can provide information about the location of a parking lot. This makes it possible to provide personalized answers based on the visitor's past question history.

[0094] The recognition unit can adjust the ticket reading method taking into account the remaining battery power of the visitor's device. For example, the recognition unit selects a method for rapid reading when the device's battery power is low. The recognition unit can also select a normal reading method when the device's battery power is sufficient. For example, the recognition unit selects a normal reading method when the device's battery power is sufficient. The recognition unit can also perform reading in low power mode when the device's battery power is low. For example, the recognition unit performs reading in low power mode when the device's battery power is low. This enables the optimal reading method to be selected according to the remaining battery power of the device.

[0095] The guidance unit can estimate the visitor's emotions and adjust the guidance display method based on the estimated visitor's emotions. For example, if the visitor is nervous, a simple, highly visible display method is provided. The guidance unit can also provide a display method that includes detailed information if the visitor is relaxed. For example, if the visitor is relaxed, the guidance unit can provide a display method that includes detailed information. If the visitor is in a hurry, the guidance unit can also provide a display method that focuses on the main points. For example, if the visitor is in a hurry, the guidance unit provides a display method that focuses on the main points. This makes it possible to display guidance that is optimal according to the visitor's emotions.

[0096] The reception unit can adjust the method for accepting questions by taking into account the visitor's current weather information. For example, if it is raining, the reception unit selects a method for quickly accepting questions so that visitors do not get wet. The reception unit can also select a normal method for accepting questions by taking into account sunny weather. For example, the reception unit selects a normal method for accepting questions by taking into account sunny weather. The reception unit can also provide a method for visitors to comfortably ask questions based on the weather information. For example, the reception unit provides a method for visitors to comfortably ask questions based on the weather information. This makes it possible to optimally accept questions by taking into account weather information.

[0097] The answering section can estimate the visitor's emotions and adjust the way the answer is expressed based on the estimated visitor's emotions. For example, if the visitor is nervous, the answering section can use simple, easy-to-understand expressions. If the visitor is relaxed, the answering section can also use expressions that include detailed information. For example, if the visitor is relaxed, the answering section can use expressions that include detailed information. If the visitor is in a hurry, the answering section can also use concise expressions that get to the point. For example, if the visitor is in a hurry, the answering section can use concise expressions that get to the point. This makes it possible to express the answer optimally according to the visitor's emotions.

[0098] The recognition unit can adjust the ticket reading method taking into account the brightness of the visitor's device screen. For example, if the device screen is dark, the recognition unit uses additional lighting to improve reading accuracy. The recognition unit can also select a normal reading method if the device screen is bright. For example, if the device screen is bright, the recognition unit selects a normal reading method. The recognition unit can also adjust the reading method according to the brightness of the device screen. For example, the recognition unit adjusts the reading method according to the brightness of the device screen. This enables the optimal reading method according to the brightness of the device screen.

[0099] The guidance unit can estimate the visitor's emotions and adjust the display speed of the guidance based on the estimated visitor's emotions. For example, if the visitor is nervous, the guidance unit can slow down the display speed of the guidance to make it easier to understand. If the visitor is relaxed, the guidance unit can also provide guidance at a normal display speed. For example, if the visitor is relaxed, the guidance unit can provide guidance at a normal display speed. If the visitor is in a hurry, the guidance unit can also speed up the display speed of the guidance to provide information quickly. For example, if the visitor is in a hurry, the guidance unit can speed up the display speed of the guidance to provide information quickly. This makes it possible to provide an optimal display speed according to the visitor's emotions.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The recognition unit reads the ticket with a camera and recognizes the seat number. For example, it scans the ticket using a smartphone camera and recognizes the seat number. When the visitor holds the ticket up to the recognition unit's camera, the generation AI analyzes the ticket format and identifies the seat number. The recognition unit can also analyze ticket formats, which vary depending on the convenience store where they were issued, and identify the seat number. For example, it can analyze tickets in different formats, such as PDF, paper, and electronic tickets, and identify the seat number. Step 2: Based on the seat number identified by the recognition unit, the guidance unit uses AR technology to display the route to the visitor's seat on the visitor's smartphone. For example, arrows and lines are displayed on the smartphone screen, allowing the visitor to follow them to their seat. The guidance unit can also display the route in 3D, 2D, or by voice guidance. Step 3: The reception desk accepts questions from visitors via the messaging app. For example, if a visitor asks a question such as "Where are the restrooms?" or "Where is the shop?" via the messaging app, the question is accepted. The reception desk can also accept questions in text format, voice format, or other formats. Step 4: In the answering section, the generation AI generates an appropriate answer based on the question received by the reception section. For example, it could provide specific directions such as "The toilets are 50 meters to the right of your seat" based on stadium map data. The answering section can use FAQ databases, natural language generation technology, etc. to clarify the criteria for appropriate answers and the generation method.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A recognition unit that reads the ticket with a camera and recognizes the seat number; a guidance unit that guides the user to his or her own seat using an augmented reality technology based on the seat number identified by the recognition unit; a reception section for accepting questions; an answering unit that generates an appropriate answer based on the question received by the receiving unit; Equipped with A system characterized by:

2. The recognition unit Parse different ticket formats and identify seat numbers 2. The system of claim 1.

3. The guide unit is Augmented reality technology is used to display the route to visitors' seats on their smartphones.

2. The system of claim 1.

4. The reception unit Accepting questions from visitors 2. The system of claim 1.

5. The answering section Generate appropriate answers based on stadium map data 2. The system of claim 1.

6. The recognition unit Estimate visitor emotions and adjust ticket reading accuracy based on the estimated emotions of the visitor.

2. The system of claim 1.

7. The recognition unit When analyzing ticket formats, refer to past reading history to select the appropriate analysis method.

2. The system of claim 1.

8. The recognition unit When reading tickets, the system adjusts the reading angle based on the visitor's current location.

2. The system of claim 1.

9. The recognition unit Estimate visitor sentiment and adjust ticket scanning speed based on estimated visitor sentiment 2. The system of claim 1.

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