system

Abstract

The system according to the embodiment aims to immediately resolve any questions or problems that users may encounter while watching a sporting event, thereby improving the viewing experience. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a provision unit, an acquisition unit, and a display unit. The reception unit receives a question from a user. The generation unit generates an answer based on the question received by the reception unit. The provision unit provides the answer generated by the generation unit. The acquisition unit acquires location information of the user. The display unit performs AR display based on the location information acquired by the acquisition 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 technologies do not adequately provide a means to quickly resolve issues when you are unable to understand the rules or the situation of a sporting event while watching it, and there is room for improvement.

[0005] The system according to the embodiment aims to immediately resolve any questions or problems that users may encounter while watching a sporting event, thereby improving the viewing experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, an acquisition unit, and a display unit. The reception unit receives a question from a user. The generation unit generates an answer based on the question received by the reception unit. The provision unit provides the answer generated by the generation unit. The acquisition unit acquires location information of the user. The display unit performs AR display based on the location information acquired by the acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can immediately resolve any questions or problems that users may encounter while watching a sporting event, thereby improving the viewing experience. [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) The AI ​​chat consultation system according to an embodiment of the present invention is a general-purpose AI chat consultation system designed to solve problems such as not understanding the rules or the current state of a sporting event. When a user asks the AI ​​what they don't understand, the AI ​​instantly responds. The AI ​​chat consultation system also features AR functions such as seating guides and venue gourmet information, allowing for a more stress-free viewing experience. For example, when a user asks the AI ​​what they don't understand, the AI ​​instantly responds. The user inputs a question using a mobile device, such as "What are the rules of this game?" or "What is the current state of play?" The AI ​​can instantly answer these questions. For example, it provides answers such as "The rules of this game are ____" or "The current state of play is ____." Next, the user can listen to the commentator's commentary while enjoying the live sound of the game. For example, the user can listen to the live game commentary and commentary in real time. This allows for a deeper understanding of the game situation. The AI ​​also features AR functions such as seating guides and venue gourmet information. Using a mobile device, users can check seating information and information about the food available at the venue using AR. For example, they can check their seat location or check information about the food available at the venue. This allows them to enjoy watching sports more stress-free. In this way, the present invention is an AI chat consultation system for the general public that solves the problem of not understanding the rules or the game situation when watching sports, allowing users to enjoy watching sports more stress-free. As a result, the AI ​​chat consultation system provides instant answers to users' questions and displays AR information based on location information, allowing users to enjoy watching sports more stress-free.

[0029] The AI ​​chat consultation system according to the embodiment includes a reception unit, a generation unit, a provision unit, an acquisition unit, and a display unit. The reception unit receives a question from a user. The question from the user may be in, for example, a text format or an audio format, but is not limited to these examples. The reception unit may receive the question from the user using, for example, a mobile device. The generation unit generates an answer based on the question received by the reception unit. The generation unit may generate an optimal answer to the question based on, for example, a database learned in advance. The generation unit may generate an optimal answer to the user's question using, for example, a machine learning algorithm. The provision unit provides the answer generated by the generation unit to the user. The provision unit may provide the answer in, for example, a text format or an audio format. The provision unit may display, for example, the generated answer on the user's mobile device. The acquisition unit acquires location information of the user. The acquisition unit may acquire location information of the user using, for example, a camera or a GPS of the mobile device. The acquisition unit may acquire location information of the user using, for example, a camera of the mobile device. The acquisition unit may acquire location information of the user using, for example, a GPS of the mobile device. The display unit performs AR display based on the location information acquired by the acquisition unit. The display unit can display, for example, seating guides and information about gourmet food in the venue in AR. The display unit can display, for example, seating guides in AR based on the acquired location information. The display unit can display, for example, gourmet food information in the venue in AR based on the acquired location information. As a result, the AI ​​chat consultation system according to the embodiment can provide instant answers to users' questions and perform AR display based on the location information, allowing users to enjoy watching sports more stress-free.

[0030] The reception unit can receive questions from users using a mobile terminal. Examples of mobile terminals include, but are not limited to, smartphones and tablets. The reception unit can receive questions from users using, for example, a smartphone. For example, a user can input a question using an app on the smartphone. The reception unit can receive questions from users using, for example, a tablet. For example, a user can input a question using an app on the tablet. This allows users to easily input questions by using a mobile terminal. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can receive questions using an AI model that receives user questions in text format.

[0031] The generation unit can generate an answer to a question based on a pre-trained database. The pre-trained database includes, for example, information on sports rules and game situations, but is not limited to such examples. The generation unit can generate an answer to a question based on, for example, a database related to sports rules. For example, when a user asks, "What are the rules of this game?", the generation unit generates an answer by referring to the database related to sports rules. The generation unit can generate an answer to a question based on, for example, a database related to game situations. For example, when a user asks, "What is the current play situation?", the generation unit generates an answer by referring to the database related to game situations. In this way, an optimal answer to a question can be provided based on the pre-trained database. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate an answer using an AI model that generates an answer based on a pre-trained database.

[0032] The providing unit can provide the generated answer to the user. The providing unit can provide the answer in, for example, text format or audio format. The providing unit can display the generated answer on the user's mobile device, for example. For example, when a user inputs a question using a smartphone, the providing unit can display the generated answer on the smartphone screen. The providing unit can provide the generated answer in audio format, for example. For example, when a user inputs a question by voice, the providing unit can provide the generated answer by voice. In this way, by providing the generated answer to the user, the user's question can be resolved. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the answer using an AI model that provides the generated answer in text format.

[0033] The acquisition unit can acquire the user's location information using a camera or GPS of the mobile device. Examples of the camera of the mobile device include, but are not limited to, a smartphone camera or a tablet camera. The acquisition unit can acquire the user's location information using, for example, a smartphone camera. For example, when a user uses the smartphone camera to check their seat location, the acquisition unit can acquire the location information through the camera. The acquisition unit can acquire the user's location information using, for example, a tablet camera. For example, when a user uses the tablet camera to check restaurant information in a venue, the acquisition unit can acquire the location information through the camera. Examples of the GPS of the mobile device include, but are not limited to, a smartphone GPS or a tablet GPS. The acquisition unit can acquire the user's location information using, for example, a smartphone GPS. For example, when a user uses the smartphone GPS to check their seat location, the acquisition unit can acquire the location information through the GPS. The acquisition unit can acquire the user's location information using, for example, a tablet GPS. For example, when a user uses the tablet GPS to check restaurant information in a venue, the acquisition unit can acquire the location information through the GPS. This allows accurate location information to be acquired by using the camera or GPS of the mobile device. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may acquire location information using an AI model that acquires location information using the camera or GPS of the mobile device.

[0034] The display unit can display seating information and restaurant information within the venue in AR based on the acquired location information. Seat information includes, but is not limited to, seat location information and route guidance, for example. The display unit can display, for example, the seat location in AR based on the acquired location information. For example, when a user uses a smartphone to check the seat location, the display unit can display the seat location in AR. The display unit can display, for example, a route to the seat in AR based on the acquired location information. For example, when a user uses a tablet to check the route to the seat, the display unit can display the route in AR. The restaurant information includes, but is not limited to, menu information, restaurant location information, reviews, and the like. The display unit can display, for example, restaurant information within the venue in AR based on the acquired location information. For example, when a user uses a smartphone to check the restaurant information within the venue, the display unit can display the restaurant information in AR. The display unit can display, for example, the restaurant location in AR based on the acquired location information. For example, when a user uses a tablet to check the restaurant location, the display unit can display the restaurant location in AR. This allows users to easily check seating information and gourmet information by displaying AR information based on location information. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit may perform AR display using an AI model that displays AR information based on the acquired location information.

[0035] The reception unit can analyze the user's past question history and select the optimal question reception method. The reception unit can, for example, store the user's past question history in a database and analyze it using AI. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that are frequently asked during a specific time period based on the user's past question history. In this way, by analyzing the past question history, the optimal question reception method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can select the optimal question reception method using an AI model that analyzes the user's past question history.

[0036] When receiving a question, the reception unit can filter the question content based on the user's current interests and concerns. The reception unit can, for example, store the user's current interests and concerns in a database and analyze them using AI. For example, if the user is interested in a particular sport, questions related to that sport can be preferentially received. Also, if the user is interested in a particular player, questions about that player can be preferentially received. Also, if the user is interested in a particular game situation, questions related to that situation can be preferentially received. In this way, by filtering the question content based on the user's interests and concerns, more relevant questions can be received. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can filter the question content using an AI model that analyzes the user's interests and concerns.

[0037] When accepting questions, the reception unit can prioritize accepting highly relevant questions based on the user's geographical location information. The reception unit can, for example, store the user's geographical location information in a database and analyze it using AI. For example, if the user is in a specific stadium, questions related to the stadium can be prioritized. Furthermore, if the user is in a specific area, questions related to the area can be prioritized. Furthermore, if the user is participating in a specific event, questions related to the event can be prioritized. In this way, by taking the user's geographical location information into consideration, highly relevant questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can prioritize accepting highly relevant questions using an AI model that analyzes the user's geographical location information.

[0038] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit can, for example, store the user's social media activity in a database and analyze it using AI. For example, if a user posts about a particular sport on social media, questions related to that sport can be preferentially received. Also, if a user posts about a particular player on social media, questions related to that player can be preferentially received. Also, if a user posts about a particular game situation on social media, questions related to that situation can be preferentially received. In this way, by analyzing the user's social media activity, related questions can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can receive related questions using an AI model that analyzes the user's social media activity.

[0039] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. The generation unit can, for example, store the importance of the question in a database and analyze it using AI. For example, if the question is about the rules of the game, a detailed explanation can be provided. Also, if the question is about the situation of the game, a concise explanation of the situation can be provided. Also, if the question is about seating information, a specific seat location can be provided. In this way, by adjusting the level of detail of the answer based on the importance of the question, a more appropriate answer can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the answer using an AI model that analyzes the importance of the question.

[0040] When generating an answer, the generation unit can apply different answering algorithms depending on the category of the question. For example, the generation unit can store the question categories in a database and analyze them using AI. For example, an algorithm that generates detailed explanations can be applied to questions about the rules. An algorithm based on real-time data can be applied to questions about the game situation. An algorithm based on location information can be applied to questions about seating arrangements. In this way, by applying different answering algorithms depending on the category of the question, more appropriate answers can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can apply different answering algorithms using an AI model that analyzes the category of the question.

[0041] When generating answers, the generation unit can determine the priority of answers based on the time when the questions were submitted. The generation unit can, for example, store the time when the questions were submitted in a database and analyze it using AI. For example, answers can be provided in real time for questions asked during a game. Furthermore, information about preparation can be provided for questions asked before the game. Furthermore, game results and highlights can be provided for questions asked after the game. In this way, by determining the priority of answers based on the time when the questions were submitted, answers can be provided at a more appropriate time. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can determine the priority of answers using an AI model that analyzes the time when the questions were submitted.

[0042] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. The generation unit can, for example, store the relevance of questions in a database and analyze it using AI. For example, questions about the rules of the game can be answered first. Questions about the game situation can be answered next. Questions about seating arrangements can be answered last. By adjusting the order of answers based on the relevance of questions, answers can be provided in a more appropriate order. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can adjust the order of answers using an AI model that analyzes the relevance of questions.

[0043] When providing an answer, the providing unit can select the optimal providing method by referring to the user's past answer history. The providing unit can, for example, store the user's past answer history in a database and analyze it using AI. For example, the providing unit can prioritize the use of a providing method (audio, text, etc.) that the user has previously preferred. The optimal method can also be suggested based on the providing methods the user has previously used. The providing unit can also select a providing method suitable for a specific time period from the user's past answer history. In this way, the optimal providing method for the user can be selected by referring to the past answer history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the optimal providing method by using an AI model that analyzes the user's past answer history.

[0044] When providing an answer, the providing unit can customize the means for providing the answer based on the user's current situation. The providing unit can, for example, store the user's current situation in a database and analyze it using AI. For example, if the user is in the middle of a game, it can provide an audio answer in real time. Also, if the user is before the game, it can provide detailed information in text. Also, if the user is after the game, it can provide a highlight video. In this way, by customizing the means for providing the answer based on the user's current situation, it is possible to provide the answer in a more appropriate manner. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the means for providing the answer using an AI model that analyzes the user's current situation.

[0045] When providing an answer, the providing unit can select the optimal providing method by taking into account the user's geographical location information. The providing unit can, for example, store the user's geographical location information in a database and analyze it using AI. For example, if the user is in a specific stadium, information related to that stadium can be preferentially provided. Also, if the user is in a specific area, information related to that area can be preferentially provided. Also, if the user is participating in a specific event, information related to that event can be preferentially provided. In this way, the optimal providing method can be selected by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can select the optimal providing method by using an AI model that analyzes the user's geographical location information.

[0046] When providing an answer, the providing unit can analyze the user's social media activity and suggest a means for providing the answer. The providing unit can, for example, store the user's social media activity in a database and analyze it using AI. For example, if the user posts about a particular sport on social media, information related to that sport can be provided preferentially. Also, if the user posts about a particular player on social media, information related to that player can be provided preferentially. Also, if the user posts about a particular game situation on social media, information related to that situation can be provided preferentially. In this way, by analyzing the user's social media activity, relevant information can be provided preferentially. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can suggest a means for providing an answer using an AI model that analyzes the user's social media activity.

[0047] The acquisition unit can analyze the user's past location information history and select the optimal acquisition method. The acquisition unit can, for example, store the user's past location information history in a database and analyze it using AI. For example, the acquisition unit can select the optimal location information acquisition method based on places the user has visited in the past. The acquisition unit can also analyze the user's past movement patterns and suggest an efficient location information acquisition method. The acquisition unit can also select an acquisition method suitable for a specific time period from the user's past location information history. In this way, the optimal location information acquisition method can be selected by analyzing the past location information history. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can select the optimal acquisition method using an AI model that analyzes the user's past location information history.

[0048] When acquiring location information, the acquisition unit can filter the location information based on the user's current situation and interests. The acquisition unit can, for example, store the user's current situation and interests in a database and analyze the information using AI. For example, if the user is participating in a specific event, location information related to the event can be preferentially acquired. Also, if the user is in a specific area, location information related to that area can be preferentially acquired. Also, if the user is interested in a specific sport, location information related to that sport can be preferentially acquired. In this way, by filtering the location information based on the user's current situation and interests, more relevant information can be acquired. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can filter the location information using an AI model that analyzes the user's current situation and interests.

[0049] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. The acquisition unit can, for example, store the user's geographical location information in a database and analyze it using AI. For example, if the user is in a specific stadium, location information related to the stadium can be prioritized. Furthermore, if the user is in a specific area, location information related to the area can be prioritized. Furthermore, if the user is participating in a specific event, location information related to the event can be prioritized. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can prioritize acquiring highly relevant information by using an AI model that analyzes the user's geographical location information.

[0050] When acquiring location information, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit can, for example, store the user's social media activity in a database and analyze it using AI. For example, if a user posts about a particular sport on social media, location information related to that sport can be acquired preferentially. Also, if a user posts about a particular player on social media, location information related to that player can be acquired preferentially. Also, if a user posts about a particular game situation on social media, location information related to that situation can be acquired preferentially. In this way, by analyzing the user's social media activity, related information can be acquired preferentially. Some or all of the above-described processing by the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can acquire related information using an AI model that analyzes the user's social media activity.

[0051] When displaying AR, the display unit can select the optimal display method by referring to the user's past display history. The display unit can, for example, store the user's past display history in a database and analyze it using AI. For example, the display unit can prioritize the use of display methods (color, design, etc.) that the user has previously preferred. The display unit can also suggest the optimal method based on the display methods the user has previously used. The display unit can also select a display method suitable for a specific time period from the user's past display history. This allows the optimal display method to be selected for the user by referring to the past display history. Some or all of the above-described processing in the display unit can be performed, for example, using AI, or can be performed without using AI. For example, the display unit can select the optimal display method using an AI model that analyzes the user's past display history.

[0052] The display unit can customize the display content based on the user's current situation when displaying AR. The display unit can, for example, store the user's current situation in a database and analyze it using AI. For example, when the user is in the middle of a game, the game status can be displayed in real time using AR. When the user is before a game, seating information can be displayed in AR. When the user is after a game, gourmet information within the venue can be displayed in AR. This allows more appropriate information to be provided by customizing the display content based on the user's current situation. Some or all of the above-mentioned processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can customize the display content using an AI model that analyzes the user's current situation.

[0053] The display unit can select the optimal display method when displaying AR information by taking into account the user's geographical location information. The display unit can, for example, store the user's geographical location information in a database and analyze it using AI. For example, if the user is in a specific stadium, information related to the stadium can be preferentially displayed in AR. Furthermore, if the user is in a specific area, information related to the area can be preferentially displayed in AR. Furthermore, if the user is participating in a specific event, information related to the event can be preferentially displayed in AR. This allows the optimal display method to be selected by taking the user's geographical location information into account. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can select the optimal display method by using an AI model that analyzes the user's geographical location information.

[0054] When displaying AR content, the display unit can analyze the user's social media activity and suggest display content. The display unit can, for example, store the user's social media activity in a database and analyze it using AI. For example, if a user posts about a specific sport on social media, information related to that sport can be preferentially displayed in AR. Furthermore, if a user posts about a specific player on social media, information related to that player can be preferentially displayed in AR. Furthermore, if a user posts about a specific game situation on social media, information related to that situation can be preferentially displayed in AR. This allows related information to be preferentially displayed by analyzing the user's social media activity. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can suggest display content using an AI model that analyzes the user's social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The generation unit can generate predicted questions in advance based on the user's past question history. For example, the next predicted question can be generated based on the content of questions frequently asked by the user in the past, and answers can be prepared before the user asks the question. The generation unit can also analyze the user's past question patterns and automatically generate related questions. Furthermore, it can predict the content of questions that will be frequently asked during a specific time period from the user's past question history and generate answers in advance. This makes it possible to prepare answers to predicted questions in advance based on the user's past question history.

[0057] The providing unit can adjust the answer providing method based on the user's current activity status. For example, if the user is moving, the answer can be provided in audio format, allowing the user to obtain information without using their hands. If the user is stationary, detailed answers can be provided in text format. Furthermore, if the user is in a specific area within a venue, information related to that area can be provided preferentially. This allows the optimal answer providing method to be selected based on the user's current activity status.

[0058] The acquisition unit can acquire the surrounding congestion status in real time based on the user's location information and provide it to the display unit. For example, when the user is in a specific area, the congestion status of that area can be acquired and displayed in AR on the display unit. Also, when the user is traveling, the congestion status of the destination can be acquired in advance and displayed on the display unit. Furthermore, when the user is participating in a specific event, the congestion status of the event can be acquired in real time and displayed on the display unit. In this way, the surrounding congestion status can be acquired in real time based on the user's location information and provided to the display unit.

[0059] The display unit can select the optimal AR display method based on the user's past display history. For example, it can prioritize the use of display methods (color, design, etc.) that the user has previously preferred. It can also suggest the optimal method based on the display methods the user has previously used. Furthermore, it can select a display method suitable for a specific time period from the user's past display history. This allows the optimal display method for the user to be selected by referring to the past display history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives a question from a user. The question from the user may be in text format, voice format, or the like. The reception unit can receive the question from the user using a mobile terminal. Step 2: The generator generates an answer based on the question received by the receiver. The generator generates an optimal answer to the question based on a pre-trained database. The generator can use a machine learning algorithm to generate an optimal answer to the user's question. Step 3: The providing unit provides the answer generated by the generating unit to the user. The providing unit can provide the answer in text format or audio format. The providing unit can display the generated answer on the user's mobile device. Step 4: The acquisition unit acquires the user's location information. The acquisition unit can acquire the user's location information using the camera or GPS of the mobile terminal. Step 5: The display unit performs AR display based on the location information acquired by the acquisition unit. The display unit can display seating information and gourmet information within the venue using AR.

[0062] (Example 2) The AI ​​chat consultation system according to an embodiment of the present invention is a general-purpose AI chat consultation system designed to solve problems such as not understanding the rules or the current state of a sporting event. When a user asks the AI ​​what they don't understand, the AI ​​instantly responds. The AI ​​chat consultation system also features AR functions such as seating guides and venue gourmet information, allowing for a more stress-free viewing experience. For example, when a user asks the AI ​​what they don't understand, the AI ​​instantly responds. The user inputs a question using a mobile device, such as "What are the rules of this game?" or "What is the current state of play?" The AI ​​can instantly answer these questions. For example, it provides answers such as "The rules of this game are ____" or "The current state of play is ____." Next, the user can listen to the commentator's commentary while enjoying the live sound of the game. For example, the user can listen to the live game commentary and commentary in real time. This allows for a deeper understanding of the game situation. The AI ​​also features AR functions such as seating guides and venue gourmet information. Using a mobile device, users can check seating information and information about the food available at the venue using AR. For example, they can check their seat location or check information about the food available at the venue. This allows them to enjoy watching sports more stress-free. In this way, the present invention is an AI chat consultation system for the general public that solves the problem of not understanding the rules or the game situation when watching sports, allowing users to enjoy watching sports more stress-free. As a result, the AI ​​chat consultation system provides instant answers to users' questions and displays AR information based on location information, allowing users to enjoy watching sports more stress-free.

[0063] The AI ​​chat consultation system according to the embodiment includes a reception unit, a generation unit, a provision unit, an acquisition unit, and a display unit. The reception unit receives a question from a user. The question from the user may be in, for example, a text format or an audio format, but is not limited to these examples. The reception unit may receive the question from the user using, for example, a mobile device. The generation unit generates an answer based on the question received by the reception unit. The generation unit may generate an optimal answer to the question based on, for example, a database learned in advance. The generation unit may generate an optimal answer to the user's question using, for example, a machine learning algorithm. The provision unit provides the answer generated by the generation unit to the user. The provision unit may provide the answer in, for example, a text format or an audio format. The provision unit may display, for example, the generated answer on the user's mobile device. The acquisition unit acquires location information of the user. The acquisition unit may acquire location information of the user using, for example, a camera or a GPS of the mobile device. The acquisition unit may acquire location information of the user using, for example, a camera of the mobile device. The acquisition unit may acquire location information of the user using, for example, a GPS of the mobile device. The display unit performs AR display based on the location information acquired by the acquisition unit. The display unit can display, for example, seating guides and information about gourmet food in the venue in AR. The display unit can display, for example, seating guides in AR based on the acquired location information. The display unit can display, for example, gourmet food information in the venue in AR based on the acquired location information. As a result, the AI ​​chat consultation system according to the embodiment can provide instant answers to users' questions and perform AR display based on the location information, allowing users to enjoy watching sports more stress-free.

[0064] The reception unit can receive questions from users using a mobile terminal. Examples of mobile terminals include, but are not limited to, smartphones and tablets. The reception unit can receive questions from users using, for example, a smartphone. For example, a user can input a question using an app on the smartphone. The reception unit can receive questions from users using, for example, a tablet. For example, a user can input a question using an app on the tablet. This allows users to easily input questions by using a mobile terminal. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can receive questions using an AI model that receives user questions in text format.

[0065] The generation unit can generate an answer to a question based on a pre-trained database. The pre-trained database includes, for example, information on sports rules and game situations, but is not limited to such examples. The generation unit can generate an answer to a question based on, for example, a database related to sports rules. For example, when a user asks, "What are the rules of this game?", the generation unit generates an answer by referring to the database related to sports rules. The generation unit can generate an answer to a question based on, for example, a database related to game situations. For example, when a user asks, "What is the current play situation?", the generation unit generates an answer by referring to the database related to game situations. In this way, an optimal answer to a question can be provided based on the pre-trained database. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate an answer using an AI model that generates an answer based on a pre-trained database.

[0066] The providing unit can provide the generated answer to the user. The providing unit can provide the answer in, for example, text format or audio format. The providing unit can display the generated answer on the user's mobile device, for example. For example, when a user inputs a question using a smartphone, the providing unit can display the generated answer on the smartphone screen. The providing unit can provide the generated answer in audio format, for example. For example, when a user inputs a question by voice, the providing unit can provide the generated answer by voice. In this way, by providing the generated answer to the user, the user's question can be resolved. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the answer using an AI model that provides the generated answer in text format.

[0067] The acquisition unit can acquire the user's location information using a camera or GPS of the mobile device. Examples of the camera of the mobile device include, but are not limited to, a smartphone camera or a tablet camera. The acquisition unit can acquire the user's location information using, for example, a smartphone camera. For example, when a user uses the smartphone camera to check their seat location, the acquisition unit can acquire the location information through the camera. The acquisition unit can acquire the user's location information using, for example, a tablet camera. For example, when a user uses the tablet camera to check restaurant information in a venue, the acquisition unit can acquire the location information through the camera. Examples of the GPS of the mobile device include, but are not limited to, a smartphone GPS or a tablet GPS. The acquisition unit can acquire the user's location information using, for example, a smartphone GPS. For example, when a user uses the smartphone GPS to check their seat location, the acquisition unit can acquire the location information through the GPS. The acquisition unit can acquire the user's location information using, for example, a tablet GPS. For example, when a user uses the tablet GPS to check restaurant information in a venue, the acquisition unit can acquire the location information through the GPS. This allows accurate location information to be acquired by using the camera or GPS of the mobile device. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may acquire location information using an AI model that acquires location information using the camera or GPS of the mobile device.

[0068] The display unit can display seating information and restaurant information within the venue in AR based on the acquired location information. Seat information includes, but is not limited to, seat location information and route guidance, for example. The display unit can display, for example, the seat location in AR based on the acquired location information. For example, when a user uses a smartphone to check the seat location, the display unit can display the seat location in AR. The display unit can display, for example, a route to the seat in AR based on the acquired location information. For example, when a user uses a tablet to check the route to the seat, the display unit can display the route in AR. The restaurant information includes, but is not limited to, menu information, restaurant location information, reviews, and the like. The display unit can display, for example, restaurant information within the venue in AR based on the acquired location information. For example, when a user uses a smartphone to check the restaurant information within the venue, the display unit can display the restaurant information in AR. The display unit can display, for example, the restaurant location in AR based on the acquired location information. For example, when a user uses a tablet to check the restaurant location, the display unit can display the restaurant location in AR. This allows users to easily check seating information and gourmet information by displaying AR information based on location information. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit may perform AR display using an AI model that displays AR information based on the acquired location information.

[0069] The reception unit can estimate the user's emotions and adjust the question reception method based on the estimated user emotions. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is excited, a simple and intuitive interface can be provided to quickly accept questions. If the user is relaxed, detailed input options can be provided and a customizable question reception method can be suggested. If the user is stressed, voice input can be prioritized to allow easy question input. This allows a more appropriate interface to be provided by adjusting the question reception method according to the user'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-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0070] The reception unit can analyze the user's past question history and select the optimal question reception method. The reception unit can, for example, store the user's past question history in a database and analyze it using AI. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that are frequently asked during a specific time period based on the user's past question history. In this way, by analyzing the past question history, the optimal question reception method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can select the optimal question reception method using an AI model that analyzes the user's past question history.

[0071] When receiving a question, the reception unit can filter the question content based on the user's current interests and concerns. The reception unit can, for example, store the user's current interests and concerns in a database and analyze them using AI. For example, if the user is interested in a particular sport, questions related to that sport can be preferentially received. Also, if the user is interested in a particular player, questions about that player can be preferentially received. Also, if the user is interested in a particular game situation, questions related to that situation can be preferentially received. In this way, by filtering the question content based on the user's interests and concerns, more relevant questions can be received. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can filter the question content using an AI model that analyzes the user's interests and concerns.

[0072] The reception unit can estimate the user's emotions and prioritize questions based on the estimated user emotions. The reception unit can, for example, capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is excited, it can prioritize urgent questions. If the user is relaxed, it can prioritize detailed questions. If the user is stressed, it can prioritize simple questions. This enables more appropriate response to questions by prioritizing questions according to the user'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-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0073] When accepting questions, the reception unit can prioritize accepting highly relevant questions based on the user's geographical location information. The reception unit can, for example, store the user's geographical location information in a database and analyze it using AI. For example, if the user is in a specific stadium, questions related to the stadium can be prioritized. Furthermore, if the user is in a specific area, questions related to the area can be prioritized. Furthermore, if the user is participating in a specific event, questions related to the event can be prioritized. In this way, by taking the user's geographical location information into consideration, highly relevant questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can prioritize accepting highly relevant questions using an AI model that analyzes the user's geographical location information.

[0074] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit can, for example, store the user's social media activity in a database and analyze it using AI. For example, if a user posts about a particular sport on social media, questions related to that sport can be preferentially received. Also, if a user posts about a particular player on social media, questions related to that player can be preferentially received. Also, if a user posts about a particular game situation on social media, questions related to that situation can be preferentially received. In this way, by analyzing the user's social media activity, related questions can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can receive related questions using an AI model that analyzes the user's social media activity.

[0075] The generation unit can estimate the user's emotions and adjust the way in which an answer is expressed based on the estimated user's emotions. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is excited, a concise and intuitive answer can be provided. If the user is relaxed, a detailed and polite answer can be provided. If the user is stressed, a simple and easy-to-understand answer can be provided. This allows for adjusting the way in which an answer is expressed depending on the user's emotions, thereby providing a more appropriate answer. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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 generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0076] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. The generation unit can, for example, store the importance of the question in a database and analyze it using AI. For example, if the question is about the rules of the game, a detailed explanation can be provided. Also, if the question is about the situation of the game, a concise explanation of the situation can be provided. Also, if the question is about seating information, a specific seat location can be provided. In this way, by adjusting the level of detail of the answer based on the importance of the question, a more appropriate answer can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the answer using an AI model that analyzes the importance of the question.

[0077] When generating an answer, the generation unit can apply different answering algorithms depending on the category of the question. For example, the generation unit can store the question categories in a database and analyze them using AI. For example, an algorithm that generates detailed explanations can be applied to questions about the rules. An algorithm based on real-time data can be applied to questions about the game situation. An algorithm based on location information can be applied to questions about seating arrangements. In this way, by applying different answering algorithms depending on the category of the question, more appropriate answers can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can apply different answering algorithms using an AI model that analyzes the category of the question.

[0078] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is excited, a short, to-the-point answer can be provided. If the user is relaxed, a longer answer with detailed explanations can be provided. If the user is stressed, a concise, easy-to-understand answer can be provided. This allows for adjusting the length of the answer according to the user's emotions to provide a more appropriate answer. 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 generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotions.

[0079] When generating answers, the generation unit can determine the priority of answers based on the time when the questions were submitted. The generation unit can, for example, store the time when the questions were submitted in a database and analyze it using AI. For example, answers can be provided in real time for questions asked during a game. Furthermore, information about preparation can be provided for questions asked before the game. Furthermore, game results and highlights can be provided for questions asked after the game. In this way, by determining the priority of answers based on the time when the questions were submitted, answers can be provided at a more appropriate time. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can determine the priority of answers using an AI model that analyzes the time when the questions were submitted.

[0080] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. The generation unit can, for example, store the relevance of questions in a database and analyze it using AI. For example, questions about the rules of the game can be answered first. Questions about the game situation can be answered next. Questions about seating arrangements can be answered last. By adjusting the order of answers based on the relevance of questions, answers can be provided in a more appropriate order. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can adjust the order of answers using an AI model that analyzes the relevance of questions.

[0081] The providing unit can estimate the user's emotions and adjust the way in which an answer is provided based on the estimated user's emotions. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is excited, the providing unit can provide an answer in a concise and intuitive manner. If the user is relaxed, the providing unit can provide an answer in a detailed and polite manner. If the user is stressed, the providing unit can provide an answer in a simple and easy-to-understand manner. This allows the providing unit to adjust the way in which an answer is provided according to the user's emotions, thereby providing an answer in a more appropriate manner. 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 providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0082] When providing an answer, the providing unit can select the optimal providing method by referring to the user's past answer history. The providing unit can, for example, store the user's past answer history in a database and analyze it using AI. For example, the providing unit can prioritize the use of a providing method (audio, text, etc.) that the user has previously preferred. The optimal method can also be suggested based on the providing methods the user has previously used. The providing unit can also select a providing method suitable for a specific time period from the user's past answer history. In this way, the optimal providing method for the user can be selected by referring to the past answer history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the optimal providing method by using an AI model that analyzes the user's past answer history.

[0083] When providing an answer, the providing unit can customize the means for providing the answer based on the user's current situation. The providing unit can, for example, store the user's current situation in a database and analyze it using AI. For example, if the user is in the middle of a game, it can provide an audio answer in real time. Also, if the user is before the game, it can provide detailed information in text. Also, if the user is after the game, it can provide a highlight video. In this way, by customizing the means for providing the answer based on the user's current situation, it is possible to provide the answer in a more appropriate manner. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the means for providing the answer using an AI model that analyzes the user's current situation.

[0084] The providing unit can estimate the user's emotions and prioritize answers based on the estimated user emotions. The providing unit can, for example, capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is excited, it can prioritize answers with high urgency. Also, if the user is relaxed, it can prioritize detailed answers. Also, if the user is stressed, it can prioritize simple answers. This allows for more appropriate answers to be provided by prioritizing answers according to the user'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 providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0085] When providing an answer, the providing unit can select the optimal providing method by taking into account the user's geographical location information. The providing unit can, for example, store the user's geographical location information in a database and analyze it using AI. For example, if the user is in a specific stadium, information related to that stadium can be preferentially provided. Also, if the user is in a specific area, information related to that area can be preferentially provided. Also, if the user is participating in a specific event, information related to that event can be preferentially provided. In this way, the optimal providing method can be selected by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can select the optimal providing method by using an AI model that analyzes the user's geographical location information.

[0086] When providing an answer, the providing unit can analyze the user's social media activity and suggest a means for providing the answer. The providing unit can, for example, store the user's social media activity in a database and analyze it using AI. For example, if the user posts about a particular sport on social media, information related to that sport can be provided preferentially. Also, if the user posts about a particular player on social media, information related to that player can be provided preferentially. Also, if the user posts about a particular game situation on social media, information related to that situation can be provided preferentially. In this way, by analyzing the user's social media activity, relevant information can be provided preferentially. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can suggest a means for providing an answer using an AI model that analyzes the user's social media activity.

[0087] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring location information based on the estimated user emotion. For example, the acquisition unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, when the user is excited, location information can be acquired frequently and updated in real time. When the user is relaxed, location information can be acquired at regular intervals. When the user is stressed, location information can be acquired at the minimum necessary timing. By adjusting the timing of acquiring location information according to the user's emotion, location information can be acquired at a more appropriate timing. 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 acquisition unit can be performed using an AI, for example, or without an AI. For example, the acquisition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0088] The acquisition unit can analyze the user's past location information history and select the optimal acquisition method. The acquisition unit can, for example, store the user's past location information history in a database and analyze it using AI. For example, the acquisition unit can select the optimal location information acquisition method based on places the user has visited in the past. The acquisition unit can also analyze the user's past movement patterns and suggest an efficient location information acquisition method. The acquisition unit can also select an acquisition method suitable for a specific time period from the user's past location information history. In this way, the optimal location information acquisition method can be selected by analyzing the past location information history. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can select the optimal acquisition method using an AI model that analyzes the user's past location information history.

[0089] When acquiring location information, the acquisition unit can filter the location information based on the user's current situation and interests. The acquisition unit can, for example, store the user's current situation and interests in a database and analyze the information using AI. For example, if the user is participating in a specific event, location information related to the event can be preferentially acquired. Also, if the user is in a specific area, location information related to that area can be preferentially acquired. Also, if the user is interested in a specific sport, location information related to that sport can be preferentially acquired. In this way, by filtering the location information based on the user's current situation and interests, more relevant information can be acquired. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can filter the location information using an AI model that analyzes the user's current situation and interests.

[0090] The acquisition unit can estimate the user's emotion and determine the priority of location information to be acquired based on the estimated user emotion. The acquisition unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is excited, it can prioritize acquiring location information with high urgency. Also, if the user is relaxed, it can prioritize acquiring detailed location information. Also, if the user is stressed, it can prioritize acquiring simple location information. This allows more appropriate information to be acquired by prioritizing location information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, an AI. For example, the acquisition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0091] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. The acquisition unit can, for example, store the user's geographical location information in a database and analyze it using AI. For example, if the user is in a specific stadium, location information related to the stadium can be prioritized. Furthermore, if the user is in a specific area, location information related to the area can be prioritized. Furthermore, if the user is participating in a specific event, location information related to the event can be prioritized. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can prioritize acquiring highly relevant information by using an AI model that analyzes the user's geographical location information.

[0092] When acquiring location information, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit can, for example, store the user's social media activity in a database and analyze it using AI. For example, if a user posts about a particular sport on social media, location information related to that sport can be acquired preferentially. Also, if a user posts about a particular player on social media, location information related to that player can be acquired preferentially. Also, if a user posts about a particular game situation on social media, location information related to that situation can be acquired preferentially. In this way, by analyzing the user's social media activity, related information can be acquired preferentially. Some or all of the above-described processing by the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can acquire related information using an AI model that analyzes the user's social media activity.

[0093] The display unit can estimate the user's emotion and adjust the AR display method based on the estimated user emotion. For example, the display unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is excited, a simple and intuitive AR display can be provided. If the user is relaxed, a detailed and attentive AR display can be provided. If the user is stressed, a simple and easy-to-understand AR display can be provided. This allows the AR display method to be adjusted according to the user's emotion, thereby providing a more appropriate display. 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-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0094] When displaying AR, the display unit can select the optimal display method by referring to the user's past display history. The display unit can, for example, store the user's past display history in a database and analyze it using AI. For example, the display unit can prioritize the use of display methods (color, design, etc.) that the user has previously preferred. The display unit can also suggest the optimal method based on the display methods the user has previously used. The display unit can also select a display method suitable for a specific time period from the user's past display history. This allows the optimal display method to be selected for the user by referring to the past display history. Some or all of the above-described processing in the display unit can be performed, for example, using AI, or can be performed without using AI. For example, the display unit can select the optimal display method using an AI model that analyzes the user's past display history.

[0095] The display unit can customize the display content based on the user's current situation when displaying AR. The display unit can, for example, store the user's current situation in a database and analyze it using AI. For example, when the user is in the middle of a game, the game status can be displayed in real time using AR. When the user is before a game, seating information can be displayed in AR. When the user is after a game, gourmet information within the venue can be displayed in AR. This allows more appropriate information to be provided by customizing the display content based on the user's current situation. Some or all of the above-mentioned processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can customize the display content using an AI model that analyzes the user's current situation.

[0096] The display unit can estimate the user's emotions and determine the priority of AR display based on the estimated user emotions. The display unit can, for example, capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is excited, it can prioritize AR display of information with high urgency. Also, if the user is relaxed, it can prioritize AR display of detailed information. Also, if the user is stressed, it can prioritize AR display of simple information. This allows more appropriate information to be provided by determining the priority of AR display according to the user'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, for example, 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 display unit can be performed using, for example, AI. For example, the display unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotions.

[0097] The display unit can select the optimal display method when displaying AR information by taking into account the user's geographical location information. The display unit can, for example, store the user's geographical location information in a database and analyze it using AI. For example, if the user is in a specific stadium, information related to the stadium can be preferentially displayed in AR. Furthermore, if the user is in a specific area, information related to the area can be preferentially displayed in AR. Furthermore, if the user is participating in a specific event, information related to the event can be preferentially displayed in AR. This allows the optimal display method to be selected by taking the user's geographical location information into account. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can select the optimal display method by using an AI model that analyzes the user's geographical location information.

[0098] When displaying AR content, the display unit can analyze the user's social media activity and suggest display content. The display unit can, for example, store the user's social media activity in a database and analyze it using AI. For example, if a user posts about a specific sport on social media, information related to that sport can be preferentially displayed in AR. Furthermore, if a user posts about a specific player on social media, information related to that player can be preferentially displayed in AR. Furthermore, if a user posts about a specific game situation on social media, information related to that situation can be preferentially displayed in AR. This allows related information to be preferentially displayed by analyzing the user's social media activity. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can suggest display content using an AI model that analyzes the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, generation unit, provision unit, acquisition unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a question from a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal answer to the question. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated answer to the user. The acquisition unit acquires user location information using, for example, the camera 42 or GPS function of the smart device 14. The display unit is realized by, for example, the display 40A of the smart device 14 and performs AR display based on the acquired location information. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, provision unit, acquisition unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a question from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal answer to the question. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated answer to the user. The acquisition unit acquires user location information using, for example, the camera 42 or GPS function of the smart glasses 214. The display unit is realized, for example, by the display of the smart glasses 214 and performs AR display based on the acquired location information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, acquisition unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives a question from a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal answer to the question. The provision unit is realized by the speaker 240 of the headset type terminal 314 and provides the generated answer to the user. The acquisition unit acquires location information of the user using, for example, the camera 42 or GPS function of the headset type terminal 314. The display unit is realized by, for example, the display 343 of the headset type terminal 314 and performs AR display based on the acquired location information. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, acquisition unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a question from a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal answer to the question. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated answer to the user. The acquisition unit acquires location information of the user using, for example, the camera 42 or GPS function of the robot 414. The display unit is realized by, for example, a display of the robot 414 and performs AR display based on the acquired location information.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The reception unit can analyze the user's voice tone and estimate the urgency of the question. For example, if the user's voice is raised, the reception unit can process the question as urgent and quickly generate an answer. If the user's voice is calm, the reception unit can process the question as a normal question and provide a detailed answer. Furthermore, if the user's voice is unstable, the reception unit can estimate that the user is stressed and provide a concise and easy-to-understand answer preferentially. This allows the reception unit to estimate the urgency of the question based on the voice tone and provide an appropriate response.

[0101] The generation unit can generate predicted questions in advance based on the user's past question history. For example, the next predicted question can be generated based on the content of questions frequently asked by the user in the past, and answers can be prepared before the user asks the question. The generation unit can also analyze the user's past question patterns and automatically generate related questions. Furthermore, it can predict the content of questions that will be frequently asked during a specific time period from the user's past question history and generate answers in advance. This makes it possible to prepare answers to predicted questions in advance based on the user's past question history.

[0102] The providing unit can adjust the answer providing method based on the user's current activity status. For example, if the user is moving, the answer can be provided in audio format, allowing the user to obtain information without using their hands. If the user is stationary, detailed answers can be provided in text format. Furthermore, if the user is in a specific area within a venue, information related to that area can be provided preferentially. This allows the optimal answer providing method to be selected based on the user's current activity status.

[0103] The acquisition unit can acquire the surrounding congestion status in real time based on the user's location information and provide it to the display unit. For example, when the user is in a specific area, the congestion status of that area can be acquired and displayed in AR on the display unit. Also, when the user is traveling, the congestion status of the destination can be acquired in advance and displayed on the display unit. Furthermore, when the user is participating in a specific event, the congestion status of the event can be acquired in real time and displayed on the display unit. In this way, the surrounding congestion status can be acquired in real time based on the user's location information and provided to the display unit.

[0104] The display unit can select the optimal AR display method based on the user's past display history. For example, it can prioritize the use of display methods (color, design, etc.) that the user has previously preferred. It can also suggest the optimal method based on the display methods the user has previously used. Furthermore, it can select a display method suitable for a specific time period from the user's past display history. This allows the optimal display method for the user to be selected by referring to the past display history.

[0105] The reception unit can estimate the user's emotions and adjust the method for receiving questions based on the estimated user emotions. For example, if the user is excited, a simple and intuitive interface can be provided to quickly receive questions. Alternatively, if the user is relaxed, detailed input options can be provided to suggest a customizable method for receiving questions. Alternatively, if the user is stressed, voice input can be prioritized to allow the user to easily input questions. In this way, a more appropriate interface can be provided by adjusting the method for receiving questions according to the user's emotions.

[0106] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is excited, a concise and intuitive answer can be provided. If the user is relaxed, a detailed and polite answer can be provided. If the user is stressed, a simple and easy-to-understand answer can be provided. In this way, by adjusting the way the answer is expressed according to the user's emotions, more appropriate answers can be provided.

[0107] The providing unit can estimate the user's emotions and adjust the method of providing an answer based on the estimated user's emotions. For example, if the user is excited, the answer can be provided in a concise and intuitive manner. If the user is relaxed, the answer can be provided in a detailed and polite manner. If the user is stressed, the answer can be provided in a simple and easy-to-understand manner. In this way, by adjusting the method of providing an answer according to the user's emotions, the answer can be provided in a more appropriate manner.

[0108] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring location information based on the estimated user emotions. For example, if the user is excited, location information can be acquired frequently and updated in real time. If the user is relaxed, location information can be acquired at regular intervals. If the user is stressed, location information can be acquired at the minimum necessary timing. In this way, by adjusting the timing of acquiring location information according to the user's emotions, location information can be acquired at more appropriate timing.

[0109] The display unit can estimate the user's emotions and adjust the AR display method based on the estimated user emotions. For example, if the user is excited, a simple and intuitive AR display can be provided. If the user is relaxed, a detailed and attentive AR display can be provided. If the user is stressed, a simple and easy-to-understand AR display can be provided. This allows the AR display method to be adjusted according to the user's emotions, making it possible to provide a more appropriate display.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The reception unit receives a question from a user. The question from the user may be in text format, voice format, or the like. The reception unit can receive the question from the user using a mobile terminal. Step 2: The generator generates an answer based on the question received by the receiver. The generator generates an optimal answer to the question based on a pre-trained database. The generator can use a machine learning algorithm to generate an optimal answer to the user's question. Step 3: The providing unit provides the answer generated by the generating unit to the user. The providing unit can provide the answer in text format or audio format. The providing unit can display the generated answer on the user's mobile device. Step 4: The acquisition unit acquires the user's location information. The acquisition unit can acquire the user's location information using the camera or GPS of the mobile terminal. Step 5: The display unit performs AR display based on the location information acquired by the acquisition unit. The display unit can display seating information and gourmet information within the venue using AR.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; a generator that generates an answer based on the question received by the receiver; a providing unit that provides the answer generated by the generating unit; an acquisition unit that acquires user location information; a display unit that performs AR display based on the location information acquired by the acquisition unit. A system characterized by:

2. The reception unit Accepting questions from users using mobile devices The system of claim 1 .

3. The generation unit Generate answers to questions based on a pre-trained database The system of claim 1 .

4. The providing unit Providing the generated answer to the user The system of claim 1 .

5. The acquisition unit Obtaining the user's location using the mobile device's camera or GPS The system of claim 1 .

6. The display unit Based on the acquired location information, seating guides and information about the food available at the venue are displayed using AR. The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the way questions are accepted based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past question history and select the optimal method for accepting questions The system of claim 1 .

Images