system

Abstract

The system according to this embodiment aims to propose an optimal seating arrangement that meets the needs and preferences of the audience. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a classification unit, and a proposal unit. The collection unit collects audience data. The generation unit generates audience profiles based on the data collected by the collection unit. The classification unit classifies the audience into segments based on the profiles generated by the generation unit. The proposal unit proposes the optimal seating arrangement based on the segments classified by the classification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, seat arrangements according to the needs and preferences of spectators have not been fully carried out, and there is room for improvement in enhancing the viewing experience.

[0005] The system according to the embodiment aims to propose an optimal seat arrangement according to the needs and preferences of spectators.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a classification unit, and a proposal unit. The collection unit collects audience data. The generation unit generates audience profiles based on the data collected by the collection unit. The classification unit classifies the audience into segments based on the profiles generated by the generation unit. The proposal unit proposes the optimal seating arrangement based on the segments classified by the classification unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal seating arrangement that meets the needs and preferences of the audience. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The seating arrangement system according to an embodiment of the present invention is a system that utilizes AI to optimally arrange seating in a baseball stadium, providing an environment where spectators can not only watch the game but also enjoy casual conversation and interaction. The seating arrangement system collects spectator data and uses generative AI to automatically generate detailed profiles that reflect the interests and preferences of the spectators. Based on the generated profiles, the system classifies spectators into different segments and proposes the optimal seating arrangement. This mechanism enables seating arrangements that meet the needs and preferences of spectators, improving the viewing experience. For example, the seating arrangement system collects and integrates spectator data from ticket purchase history, survey results, and SNS posts. At this time, it collects detailed data such as which games spectators watched, which seats they chose, and their impressions and opinions during the game. For example, it collects information from SNS posts such as which players spectators are interested in and which areas they would like to watch from. This makes it possible to understand the interests and preferences of the spectators. Next, the seating arrangement system uses generative AI to automatically generate detailed profiles that reflect the interests and preferences of the spectators. The generative AI analyzes the collected data to identify the interests and preferences of the spectators. For example, the system generates profiles that reflect the characteristics of the audience, such as spectators who are interested in specific players or families attending the game. This allows for seating arrangements tailored to the needs of the audience. Based on the generated profiles, the system classifies the audience into different segments and proposes optimal seating arrangements. For example, it suggests areas where children can enjoy themselves for families, and seats closer to the players for avid fans. In this way, seating arrangements that meet the needs and preferences of the audience are realized. This improves the viewing experience. Spectators can watch the game from seats that suit their interests and preferences, and can easily enjoy chatting and interacting with others. For example, families can watch the game in areas where children can enjoy themselves while interacting with other families. Also, avid fans can cheer together with other fans from seats closer to the players. This increases audience satisfaction and makes the viewing experience more fulfilling. In this way, the seating arrangement system can realize seating arrangements that meet the needs and preferences of the audience, improving the viewing experience.

[0029] The seating arrangement system according to the embodiment comprises a collection unit, a generation unit, a classification unit, and a proposal unit. The collection unit collects spectator data. Spectator data includes, but is not limited to, ticket purchase history, survey results, and social media posts. For example, the collection unit collects spectators' viewing history based on ticket purchase history. The collection unit can also collect spectators' interests and preferences based on survey results. The collection unit can also collect spectators' impressions and opinions based on social media posts. For example, the collection unit collects detailed data such as which matches spectators watched, which seats they chose, and their impressions and opinions during the viewing. The generation unit uses generation AI to generate spectator profiles based on the data collected by the collection unit. For example, the generation unit analyzes the collected data to identify spectators' interests and preferences. For example, the generation unit generates profiles that reflect the characteristics of spectators, such as spectators who are interested in a particular player or spectators who watch with their families. The generation unit uses generation AI to automatically generate detailed profiles that reflect spectators' interests and preferences. The classification unit classifies spectators into segments based on profiles generated by the generation unit. The classification unit classifies spectators into different segments, such as families, avid fans, and first-time spectators. Based on the characteristics of the spectators, the classification unit classifies them into segments for suggesting optimal seating arrangements. The suggestion unit proposes optimal seating arrangements based on the segments classified by the classification unit. For example, the suggestion unit suggests areas where children can enjoy themselves for families. The suggestion unit can also suggest seats closer to the players for avid fans. The suggestion unit can also suggest seats that are easy to watch from for first-time spectators. For example, the suggestion unit suggests seats where families can watch the game while interacting with other families in an area where children can enjoy themselves. For avid fans, it suggests seats closer to the players where they can cheer together with other fans. For first-time spectators, it suggests seats that are easy to watch from and where they can enjoy the game. In this way, the spectator seating arrangement system according to the embodiment can realize seating arrangements that meet the needs and preferences of spectators and improve the viewing experience.

[0030] The data collection department collects spectator data. This data includes, but is not limited to, ticket purchase history, survey results, and social media posts. For example, the department collects spectator attendance history based on ticket purchase history. Specifically, it collects detailed data such as which matches spectators have attended in the past, which seats they chose, and what kind of tickets they purchased. The data collection department can also collect spectators' interests and preferences based on survey results. Surveys include questions about spectators' favorite players or teams, preferred seating locations during matches, and activities they would like to enjoy while watching a match. Furthermore, the data collection department can collect spectators' impressions and opinions based on social media posts. Social media posts include what spectators felt during the match, their impressions after watching the match, and their interactions with other spectators. In this way, the data collection department can collect diverse data on spectators and understand detailed information about their interests, preferences, and viewing experiences. The data collection department manages this data centrally and can link with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the generation and classification units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The generation unit uses a generation AI to generate audience profiles based on data collected by the collection unit. For example, the generation unit analyzes the collected data to identify audience interests and preferences. Specifically, the generation AI analyzes audience ticket purchase history, survey results, and social media posts to automatically generate detailed profiles that reflect audience interests and preferences. For example, it generates profiles that reflect the characteristics of audience members, such as audience members who are interested in a particular player or audience members who attend games with their families. The generation AI uses natural language processing technology to analyze social media posts and extract audience comments and opinions. It can also use machine learning algorithms to analyze past behavioral patterns and interest trends of audience members and predict future interests and preferences. As a result, the generation unit can generate detailed profiles that reflect audience interests and preferences and provide basic data for suggesting seating arrangements that meet audience needs. Furthermore, the generation unit can continuously update the generated profiles to maintain accurate profiles based on the latest data. For example, each time new viewing history, survey results, or social media posts are collected, the generation AI re-analyzes the profile to reflect the latest information. This allows the generation unit to always provide accurate profiles based on the latest information, enabling optimal seating arrangements that meet the needs of the audience.

[0032] The classification unit categorizes spectators into segments based on the profiles generated by the generation unit. For example, the classification unit categorizes spectators into different segments such as families, avid fans, and first-time attendees. Specifically, it analyzes the generated profiles and classifies them into the optimal segment based on their characteristics, interests, and preferences. For instance, families should be seated in areas with plenty of activities and facilities for children, avid fans in seats close to the players or in areas where cheering is most energetic, and first-time attendees in areas with easy-to-view seats or ample guidance. The classification unit then categorizes spectators into segments based on their characteristics to propose optimal seating arrangements. The classification unit uses machine learning algorithms to analyze spectator profiles and classify them into optimal segments. For example, it uses clustering algorithms to divide spectator profiles into multiple clusters and analyze the characteristics of spectators belonging to each cluster. This allows the classification unit to identify optimal segments based on spectator characteristics and provide foundational data for proposing seating arrangements that meet spectator needs. Furthermore, the classification unit continuously updates the segment classification results to maintain accurate segments based on the latest data. This allows the classification unit to provide accurate segments based on the latest information at all times, enabling optimal seating arrangements that meet the needs of the audience.

[0033] The proposal department proposes optimal seating arrangements based on segments classified by the classification department. For example, for families, the proposal department suggests areas where children can enjoy themselves. Specifically, it suggests areas with plenty of activities and facilities for children, or areas where families can watch the game while interacting with other families. The proposal department can also suggest seats closer to the players for avid fans. For avid fans, it suggests seats where they can watch the players up close, or areas where they can cheer together with other fans. Furthermore, the proposal department can suggest seats that are easy to watch for first-time spectators. For first-time spectators, it suggests seats with a view of the entire game or areas with ample information. To propose the optimal seating arrangement that meets the needs and preferences of the spectators, the proposal department uses generative AI to simulate multiple scenarios. For example, it generates multiple seating arrangement scenarios based on spectator profiles and segments, and evaluates the advantages and disadvantages of each scenario. This allows the proposal department to identify and propose the most appropriate seating arrangement to the spectators. In addition, the proposal department can continuously revise its proposals based on real-time updated data to respond to the latest situations. For example, if new spectator data or changes in the match situation occur, the proposal department immediately incorporates the new data and updates the proposals. This allows the proposal department to always provide optimal seating arrangements based on the latest information, improving the viewing experience to meet the needs of spectators.

[0034] The data collection unit can collect data such as ticket purchase history, survey results, and social media posts. For example, the data collection unit can collect spectators' viewing history based on ticket purchase history. For example, the data collection unit can collect data such as purchase date and time, purchase location, and number of tickets purchased. The data collection unit can also collect spectators' interests and preferences based on survey results. For example, the data collection unit can collect data such as question items, answer formats, and respondent attributes. The data collection unit can also collect spectators' impressions and opinions based on social media posts. For example, the data collection unit can collect data such as post content, posting date and time, and poster attributes. By collecting diverse data on spectators in this way, it is possible to provide basic data for generating detailed profiles. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input spectators' social media posts into AI, which can analyze the post content to extract spectators' interests and preferences.

[0035] The generation unit can generate detailed profiles that reflect the interests and preferences of spectators based on the collected data. For example, the generation unit analyzes the collected data to identify the interests and preferences of spectators. For example, the generation unit generates profiles that reflect the characteristics of spectators, such as spectators who are interested in a particular player or spectators who are watching with their families. The generation unit uses a generation AI to automatically generate detailed profiles that reflect the interests and preferences of spectators. For example, the generation unit inputs spectator data into the generation AI, and the generation AI analyzes the interests and preferences of the spectators to generate profiles. This allows for seating arrangements that match the needs of spectators by generating detailed profiles that reflect their interests and preferences. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs spectator data into the generation AI, and the generation AI analyzes the interests and preferences of the spectators to generate profiles.

[0036] The classification unit can classify spectators into different segments based on the generated profiles. For example, the classification unit can classify spectators into different segments such as families, avid fans, and first-time spectators. Based on the characteristics of the spectators, the classification unit classifies them into segments to suggest the optimal seating arrangement. For example, the classification unit can classify families into a segment that places them in an area where children can enjoy themselves. The classification unit can also classify avid fans into a segment that places them in seats closer to the players. Furthermore, the classification unit can classify first-time spectators into a segment that places them in seats that offer a good view. By classifying spectators into different segments, seating arrangements can be made that meet the needs and preferences of the spectators. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input the generated profiles into AI, which can then classify the spectators into different segments.

[0037] The suggestion department can suggest areas where children can enjoy themselves for families and seats closer to the players for avid fans. For example, for families, the suggestion department can suggest seats in areas where children can enjoy themselves and interact with other families. For example, the suggestion department can suggest seats considering the presence of playground equipment, children's events, and family-friendly services. Also, for avid fans, the suggestion department can suggest seats closer to the players where they can cheer together with other fans. For example, the suggestion department can suggest seats considering fieldside, near the bench, or in specific zones. Furthermore, the suggestion department can suggest seats that are easy to watch for first-time spectators. For example, the suggestion department can suggest seats considering the spectator's viewpoint, sound effects, and ease of access. In this way, the viewing experience is improved by suggesting seating arrangements that meet the needs and preferences of the spectators. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input spectator profiles into AI, and the AI ​​can suggest the optimal seating arrangement.

[0038] The data collection unit can analyze spectators' past viewing history and select the optimal data collection method. For example, based on data from matches spectators have watched in the past, the data collection unit can identify which matches were particularly interesting and prioritize the collection of data related to those matches. For example, the data collection unit can analyze responses to surveys spectators have taken in the past, identify which questions provided the most useful information, and reuse similar questions. The data collection unit can also analyze what spectators have posted on social media in the past and prioritize the collection of posts containing specific hashtags or keywords. This allows for more effective data collection by analyzing spectators' past viewing history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input spectators' past viewing history into AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter data based on the audience's current interests and preferences during data collection. For example, if an audience member is interested in a particular player, the data collection unit will prioritize collecting data related to that player. For example, if an audience member is interested in the highlights of a particular match, the data collection unit will prioritize collecting data related to the highlights of that match. The data collection unit can also prioritize collecting data related to a particular event or promotion if an audience member is interested in that event or promotion. This allows for the collection of more relevant data by filtering the data based on the audience's current interests and preferences. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the audience's current interests and preferences into the AI, which can then filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of spectators during data collection. For example, if a spectator is in a specific area of ​​the stadium, the data collection unit will prioritize the collection of data related to that area. For example, if a spectator is from a specific city or region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if a spectator is from a specific country, the data collection unit can prioritize the collection of data related to that country. This allows for the collection of more relevant data by considering the geographical location information of spectators. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of spectators into the AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the audience's social media activity and collect relevant data during data collection. For example, if the audience uses a specific hashtag, the data collection unit will prioritize collecting data related to that hashtag. For example, if the audience follows a specific account, the data collection unit will prioritize collecting data related to that account. The data collection unit can also prioritize collecting data related to a post if the audience shares a specific post. This allows for the collection of more relevant data by analyzing the audience's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the audience's social media activity into an AI, which can then collect the relevant data.

[0042] The generation unit can adjust the level of detail in a profile based on the audience's level of interest during profile generation. For example, if an audience member has a strong interest in a particular player, the generation unit can generate a profile that includes detailed information about that player. For example, the generation unit can analyze audience data, extract information about the specific player, and reflect it in the profile. The generation unit can also generate a profile that includes an overview of the entire match if the audience member is interested in the match as a whole. For example, the generation unit can include information about match highlights and important plays in the profile. The generation unit can also generate a profile that includes detailed information about an event if the audience member is interested in a particular event. For example, the generation unit can include information about the event's schedule and participants in the profile. This allows for the generation of more relevant profiles by adjusting the level of detail in the profile based on the audience member's level of interest. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the audience member's level of interest into the generation AI, which can then adjust the level of detail in the profile.

[0043] The generation unit can apply different generation algorithms depending on the spectator category when generating profiles. For example, the generation unit can generate a profile that emphasizes family-friendly information for families. For example, the generation unit can analyze spectator data, extract family-friendly information, and reflect it in the profile. The generation unit can also generate a profile that includes detailed information about players for avid fans. For example, the generation unit can analyze spectator data, extract information about players, and reflect it in the profile. Furthermore, the generation unit can generate a profile that includes basic information for first-time spectators. For example, the generation unit can analyze spectator data and include basic match information and viewing points in the profile. This allows for the generation of more appropriate profiles by applying different generation algorithms depending on the spectator category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the spectator category into the generation AI, and the generation AI can apply different generation algorithms.

[0044] The generation unit can determine the priority of profiles based on the timing of audience data collection when generating profiles. For example, the generation unit can generate profiles based on recently collected audience data. For example, the generation unit analyzes audience data and prioritizes reflecting recent data in the profile. The generation unit can also generate profiles based on past audience data. For example, the generation unit analyzes audience data and reflects past data in the profile. The generation unit can also generate profiles based on data related to specific events or matches. For example, the generation unit analyzes audience data and reflects information related to specific events or matches in the profile. This allows for the generation of more relevant profiles by determining the priority of profiles based on the timing of audience data collection. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the timing of audience data collection into the generation AI, and the generation AI can determine the priority of profiles.

[0045] The generation unit can adjust the order of profiles based on the relevance of the audience members when generating profiles. For example, if an audience member is interested in a particular player, the generation unit will display information about that player first. For example, the generation unit can analyze audience data and place information about the specific player at the beginning of the profile. The generation unit can also display information about a particular match first if an audience member is interested in that match. For example, the generation unit can analyze audience data and place information about the particular match at the beginning of the profile. The generation unit can also display information about an event first if an audience member is interested in that event. For example, the generation unit can analyze audience data and place information about the particular event at the beginning of the profile. By adjusting the order of profiles based on the relevance of the audience members, more relevant profiles can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input audience relevance into a generation AI, and the generation AI can adjust the order of the profiles.

[0046] The classification unit can improve the accuracy of its classification by considering the relationships between audience members. For example, if an audience member is a family, the classification unit can classify the entire family as one group. For example, the classification unit analyzes the audience data, identifies family relationships, and classifies the entire family as one group. The classification unit can also classify if an audience member is a group of friends. For example, the classification unit analyzes the audience data, identifies friendships, and classifies the entire group of friends. Furthermore, if audience members share the same interests, the classification unit can classify them as a group based on those interests. For example, the classification unit analyzes the audience data and classifies audience members with common interests as a group. This allows for more accurate classification by considering the relationships between audience members. Some or all of the above processing in the classification unit may be performed using AI, or it may be performed without AI. For example, the classification unit can input the relationships between audience members into the AI, which can then improve the accuracy of the classification.

[0047] The classification unit can perform classification while considering the attribute information of the audience. For example, the classification unit can classify based on the age of the audience. For example, the classification unit can analyze the audience data and classify the audience by age group. The classification unit can also classify based on the gender of the audience. For example, the classification unit can analyze the audience data and classify the audience by gender. The classification unit can also classify based on the place of residence of the audience. For example, the classification unit can analyze the audience data and classify the audience by place of residence. This allows for more appropriate classification by considering the attribute information of the audience. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input the attribute information of the audience into AI, and the AI ​​can perform the classification.

[0048] The classification unit can perform classification while considering the geographical distribution of the audience. For example, if the audience comes from a specific region, the classification unit can classify them based on that region. For example, the classification unit can analyze the audience data and classify the audience by specific region. The classification unit can also classify the audience based on a specific city if the audience comes from that city. For example, the classification unit can analyze the audience data and classify the audience by specific city. The classification unit can also classify the audience based on a specific country if the audience comes from that country. For example, the classification unit can analyze the audience data and classify the audience by specific country. This allows for more appropriate classification by considering the geographical distribution of the audience. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input the geographical distribution of the audience into AI, and the AI ​​can perform the classification.

[0049] The classification unit can improve the accuracy of its classification by referring to relevant literature for the audience during the classification process. For example, if the audience has referred to literature about a specific player, the classification unit can classify based on that literature. For example, the classification unit can analyze the audience data and classify based on literature about a specific player. The classification unit can also classify based on literature if the audience has referred to literature about a specific match. For example, the classification unit can analyze the audience data and classify based on literature about a specific match. The classification unit can also classify based on literature if the audience has referred to literature about a specific event. For example, the classification unit can analyze the audience data and classify based on literature about a specific event. This allows for more accurate classification by referring to relevant literature for the audience. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input relevant literature for the audience into the AI, which can then improve the accuracy of the classification.

[0050] The suggestion function can adjust the level of detail in its suggestions based on the audience's level of interest. For example, if an audience member has a strong interest in a particular player, the suggestion function can provide a suggestion that includes detailed information about that player. For instance, the suggestion function can analyze audience data, extract information about the specific player, and incorporate it into the suggestion. Alternatively, if an audience member is interested in the entire match, the suggestion function can provide a suggestion that includes an overview of the entire match. For example, the suggestion function can analyze audience data and include information about match highlights and important plays in the suggestion. Furthermore, if an audience member is interested in a particular event, the suggestion function can provide a suggestion that includes detailed information about that event. For example, the suggestion function can analyze audience data and include information about the event's schedule and participants in the suggestion. This allows for more relevant suggestions by adjusting the level of detail based on the audience's level of interest. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input the audience's level of interest into the AI, which can then adjust the level of detail in the suggestion.

[0051] The suggestion unit can apply different suggestion algorithms depending on the audience category when making suggestions. For example, the suggestion unit can make suggestions that emphasize family-friendly information to families. For example, the suggestion unit can analyze audience data, extract family-friendly information, and incorporate it into the suggestions. The suggestion unit can also make suggestions that include detailed information about players to avid fans. For example, the suggestion unit can analyze audience data, extract information about players, and incorporate it into the suggestions. Furthermore, the suggestion unit can make suggestions that include basic information to first-time spectators. For example, the suggestion unit can analyze audience data and include basic match information and viewing points in the suggestions. This allows for more appropriate suggestions by applying different suggestion algorithms depending on the audience category. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input audience categories into the AI, and the AI ​​can apply different suggestion algorithms.

[0052] The proposal department can prioritize proposals based on the timing of audience data collection. For example, the proposal department can make proposals based on recently collected audience data. For example, the proposal department can analyze audience data and prioritize the incorporation of recent data into proposals. The proposal department can also make proposals based on past audience data. For example, the proposal department can analyze audience data and incorporate past data into proposals. The proposal department can also make proposals based on data related to specific events or matches. For example, the proposal department can analyze audience data and incorporate information related to specific events or matches into proposals. This allows for more relevant proposals by prioritizing proposals based on the timing of audience data collection. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the timing of audience data collection into AI, and the AI ​​can determine the priority of proposals.

[0053] The suggestion unit can adjust the order of suggestions based on the audience's relevance. For example, if an audience member is interested in a particular player, the suggestion unit will suggest information about that player first. For example, the suggestion unit can analyze audience data and place information about the specific player at the beginning of the suggestions. The suggestion unit can also suggest information about a particular match first if an audience member is interested in that match. For example, the suggestion unit can analyze audience data and place information about the specific match at the beginning of the suggestions. The suggestion unit can also suggest information about an event first if an audience member is interested in that event. For example, the suggestion unit can analyze audience data and place information about the specific event at the beginning of the suggestions. By adjusting the order of suggestions based on the audience's relevance, more relevant suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input audience relevance into AI, and the AI ​​can adjust the order of suggestions.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The spectator seating arrangement system can monitor the health status of spectators and adjust seating arrangements based on their health. For example, the data collection unit can collect biometric data such as spectators' heart rate and blood pressure. The data generation unit analyzes the collected biometric data and evaluates the health status of the spectators. The classification unit classifies spectators into different segments based on their health status. For example, it can suggest areas with many stairs to spectators in good health and areas with easy access to seating to spectators in unstable health. This enables seating arrangements tailored to the health status of spectators, improving the viewing experience.

[0056] The seating arrangement system can analyze spectators' past purchase history and suggest seating arrangements based on their purchasing trends for specific products. For example, the collection unit collects spectators' past purchase history of food, beverages, and merchandise. The generation unit analyzes the collected data to identify spectators' purchasing trends. The classification unit categorizes spectators into different segments based on their purchasing trends. For example, it can suggest areas where certain food and beverages are served to spectators who prefer specific merchandise, and suggest seats near merchandise booths to spectators who prefer certain goods. This enables seating arrangements tailored to spectators' purchasing trends, improving the viewing experience.

[0057] The spectator seating arrangement system can propose events to promote interaction among spectators based on their hobbies and interests. For example, the data collection unit collects spectator survey results and social media posts to identify their hobbies and interests. The generation unit analyzes the collected data and generates profiles based on the spectators' hobbies and interests. The classification unit categorizes spectators into different segments based on their hobbies and interests. The proposal unit can then propose events to promote interaction among spectators based on the classified segments. This promotes interaction among spectators and improves the viewing experience.

[0058] The seating arrangement system can analyze spectators' past viewing history and suggest seating arrangements based on their interest in specific matches or events. For example, the collection unit collects spectators' past viewing history and identifies which matches or events they attended. The generation unit analyzes the collected data to identify spectators' interests. The classification unit categorizes spectators into different segments based on their interests. The suggestion unit can suggest areas related to specific matches or events to spectators who are interested in those matches or events. This results in seating arrangements tailored to spectators' interests, improving the viewing experience.

[0059] The seating arrangement system can seat spectators from the same region near each other based on their geographical origin. For example, the collection unit collects spectator address data and identifies their region of origin. The generation unit analyzes the collected data to identify the spectators' place of origin. The classification unit categorizes spectators into different segments based on their place of origin. The proposal unit can promote interaction among spectators by seating them near each other. This promotes interaction among spectators and improves the viewing experience.

[0060] The spectator seating arrangement system can analyze past spectator feedback and propose seating arrangements based on that feedback. For example, the collection unit collects past survey results and social media posts from spectators and identifies feedback. The generation unit analyzes the collected data and identifies spectator feedback. The classification unit classifies spectators into different segments based on the feedback. The proposal unit can propose seating arrangements that meet spectator needs based on past feedback. This results in seating arrangements that respond to spectator feedback and improves the viewing experience.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects audience data. This data includes ticket purchase history, survey results, and social media posts. For example, the data collection unit collects audience attendance history based on ticket purchase history, gathers audience interests and preferences based on survey results, and collects audience impressions and opinions based on social media posts. Step 2: The generation unit uses generation AI to generate audience profiles based on the data collected by the collection unit. The generation unit analyzes the collected data to identify the audience's interests and preferences. For example, it generates profiles that reflect the characteristics of the audience, such as audience members who are interested in a particular player or audience members who are watching the game with their families. Step 3: The classification unit divides the audience into segments based on the profiles generated by the generation unit. The classification unit divides the audience into different segments such as families, avid fans, and first-time spectators. This divides the audience into segments that can be used to suggest the optimal seating arrangement based on their characteristics. Step 4: The proposal department proposes the optimal seating arrangement based on the segments classified by the classification department. For example, it might suggest areas where children can enjoy themselves for families, seats closer to the players for avid fans, and seats that are easy to watch for first-time spectators.

[0063] (Example of form 2) The seating arrangement system according to an embodiment of the present invention is a system that utilizes AI to optimally arrange seating in a baseball stadium, providing an environment where spectators can not only watch the game but also enjoy casual conversation and interaction. The seating arrangement system collects spectator data and uses generative AI to automatically generate detailed profiles that reflect the interests and preferences of the spectators. Based on the generated profiles, the system classifies spectators into different segments and proposes the optimal seating arrangement. This mechanism enables seating arrangements that meet the needs and preferences of spectators, improving the viewing experience. For example, the seating arrangement system collects and integrates spectator data from ticket purchase history, survey results, and SNS posts. At this time, it collects detailed data such as which games spectators watched, which seats they chose, and their impressions and opinions during the game. For example, it collects information from SNS posts such as which players spectators are interested in and which areas they would like to watch from. This makes it possible to understand the interests and preferences of the spectators. Next, the seating arrangement system uses generative AI to automatically generate detailed profiles that reflect the interests and preferences of the spectators. The generative AI analyzes the collected data to identify the interests and preferences of the spectators. For example, the system generates profiles that reflect the characteristics of the audience, such as spectators who are interested in specific players or families attending the game. This allows for seating arrangements tailored to the needs of the audience. Based on the generated profiles, the system classifies the audience into different segments and proposes optimal seating arrangements. For example, it suggests areas where children can enjoy themselves for families, and seats closer to the players for avid fans. In this way, seating arrangements that meet the needs and preferences of the audience are realized. This improves the viewing experience. Spectators can watch the game from seats that suit their interests and preferences, and can easily enjoy chatting and interacting with others. For example, families can watch the game in areas where children can enjoy themselves while interacting with other families. Also, avid fans can cheer together with other fans from seats closer to the players. This increases audience satisfaction and makes the viewing experience more fulfilling. In this way, the seating arrangement system can realize seating arrangements that meet the needs and preferences of the audience, improving the viewing experience.

[0064] The seating arrangement system according to the embodiment comprises a collection unit, a generation unit, a classification unit, and a proposal unit. The collection unit collects spectator data. Spectator data includes, but is not limited to, ticket purchase history, survey results, and social media posts. For example, the collection unit collects spectators' viewing history based on ticket purchase history. The collection unit can also collect spectators' interests and preferences based on survey results. The collection unit can also collect spectators' impressions and opinions based on social media posts. For example, the collection unit collects detailed data such as which matches spectators watched, which seats they chose, and their impressions and opinions during the viewing. The generation unit uses generation AI to generate spectator profiles based on the data collected by the collection unit. For example, the generation unit analyzes the collected data to identify spectators' interests and preferences. For example, the generation unit generates profiles that reflect the characteristics of spectators, such as spectators who are interested in a particular player or spectators who watch with their families. The generation unit uses generation AI to automatically generate detailed profiles that reflect spectators' interests and preferences. The classification unit classifies spectators into segments based on profiles generated by the generation unit. The classification unit classifies spectators into different segments, such as families, avid fans, and first-time spectators. Based on the characteristics of the spectators, the classification unit classifies them into segments for suggesting optimal seating arrangements. The suggestion unit proposes optimal seating arrangements based on the segments classified by the classification unit. For example, the suggestion unit suggests areas where children can enjoy themselves for families. The suggestion unit can also suggest seats closer to the players for avid fans. The suggestion unit can also suggest seats that are easy to watch from for first-time spectators. For example, the suggestion unit suggests seats where families can watch the game while interacting with other families in an area where children can enjoy themselves. For avid fans, it suggests seats closer to the players where they can cheer together with other fans. For first-time spectators, it suggests seats that are easy to watch from and where they can enjoy the game. In this way, the spectator seating arrangement system according to the embodiment can realize seating arrangements that meet the needs and preferences of spectators and improve the viewing experience.

[0065] The data collection department collects spectator data. This data includes, but is not limited to, ticket purchase history, survey results, and social media posts. For example, the department collects spectator attendance history based on ticket purchase history. Specifically, it collects detailed data such as which matches spectators have attended in the past, which seats they chose, and what kind of tickets they purchased. The data collection department can also collect spectators' interests and preferences based on survey results. Surveys include questions about spectators' favorite players or teams, preferred seating locations during matches, and activities they would like to enjoy while watching a match. Furthermore, the data collection department can collect spectators' impressions and opinions based on social media posts. Social media posts include what spectators felt during the match, their impressions after watching the match, and their interactions with other spectators. In this way, the data collection department can collect diverse data on spectators and understand detailed information about their interests, preferences, and viewing experiences. The data collection department manages this data centrally and can link with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the generation and classification units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The generation unit uses a generation AI to generate audience profiles based on data collected by the collection unit. For example, the generation unit analyzes the collected data to identify audience interests and preferences. Specifically, the generation AI analyzes audience ticket purchase history, survey results, and social media posts to automatically generate detailed profiles that reflect audience interests and preferences. For example, it generates profiles that reflect the characteristics of audience members, such as audience members who are interested in a particular player or audience members who attend games with their families. The generation AI uses natural language processing technology to analyze social media posts and extract audience comments and opinions. It can also use machine learning algorithms to analyze past behavioral patterns and interest trends of audience members and predict future interests and preferences. As a result, the generation unit can generate detailed profiles that reflect audience interests and preferences and provide basic data for suggesting seating arrangements that meet audience needs. Furthermore, the generation unit can continuously update the generated profiles to maintain accurate profiles based on the latest data. For example, each time new viewing history, survey results, or social media posts are collected, the generation AI re-analyzes the profile to reflect the latest information. This allows the generation unit to always provide accurate profiles based on the latest information, enabling optimal seating arrangements that meet the needs of the audience.

[0067] The classification unit categorizes spectators into segments based on the profiles generated by the generation unit. For example, the classification unit categorizes spectators into different segments such as families, avid fans, and first-time attendees. Specifically, it analyzes the generated profiles and classifies them into the optimal segment based on their characteristics, interests, and preferences. For instance, families should be seated in areas with plenty of activities and facilities for children, avid fans in seats close to the players or in areas where cheering is most energetic, and first-time attendees in areas with easy-to-view seats or ample guidance. The classification unit then categorizes spectators into segments based on their characteristics to propose optimal seating arrangements. The classification unit uses machine learning algorithms to analyze spectator profiles and classify them into optimal segments. For example, it uses clustering algorithms to divide spectator profiles into multiple clusters and analyze the characteristics of spectators belonging to each cluster. This allows the classification unit to identify optimal segments based on spectator characteristics and provide foundational data for proposing seating arrangements that meet spectator needs. Furthermore, the classification unit continuously updates the segment classification results to maintain accurate segments based on the latest data. This allows the classification unit to provide accurate segments based on the latest information at all times, enabling optimal seating arrangements that meet the needs of the audience.

[0068] The proposal department proposes optimal seating arrangements based on segments classified by the classification department. For example, for families, the proposal department suggests areas where children can enjoy themselves. Specifically, it suggests areas with plenty of activities and facilities for children, or areas where families can watch the game while interacting with other families. The proposal department can also suggest seats closer to the players for avid fans. For avid fans, it suggests seats where they can watch the players up close, or areas where they can cheer together with other fans. Furthermore, the proposal department can suggest seats that are easy to watch for first-time spectators. For first-time spectators, it suggests seats with a view of the entire game or areas with ample information. To propose the optimal seating arrangement that meets the needs and preferences of the spectators, the proposal department uses generative AI to simulate multiple scenarios. For example, it generates multiple seating arrangement scenarios based on spectator profiles and segments, and evaluates the advantages and disadvantages of each scenario. This allows the proposal department to identify and propose the most appropriate seating arrangement to the spectators. In addition, the proposal department can continuously revise its proposals based on real-time updated data to respond to the latest situations. For example, if new spectator data or changes in the match situation occur, the proposal department immediately incorporates the new data and updates the proposals. This allows the proposal department to always provide optimal seating arrangements based on the latest information, improving the viewing experience to meet the needs of spectators.

[0069] The data collection unit can collect data such as ticket purchase history, survey results, and social media posts. For example, the data collection unit can collect spectators' viewing history based on ticket purchase history. For example, the data collection unit can collect data such as purchase date and time, purchase location, and number of tickets purchased. The data collection unit can also collect spectators' interests and preferences based on survey results. For example, the data collection unit can collect data such as question items, answer formats, and respondent attributes. The data collection unit can also collect spectators' impressions and opinions based on social media posts. For example, the data collection unit can collect data such as post content, posting date and time, and poster attributes. By collecting diverse data on spectators in this way, it is possible to provide basic data for generating detailed profiles. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input spectators' social media posts into AI, which can analyze the post content to extract spectators' interests and preferences.

[0070] The generation unit can generate detailed profiles that reflect the interests and preferences of spectators based on the collected data. For example, the generation unit analyzes the collected data to identify the interests and preferences of spectators. For example, the generation unit generates profiles that reflect the characteristics of spectators, such as spectators who are interested in a particular player or spectators who are watching with their families. The generation unit uses a generation AI to automatically generate detailed profiles that reflect the interests and preferences of spectators. For example, the generation unit inputs spectator data into the generation AI, and the generation AI analyzes the interests and preferences of the spectators to generate profiles. This allows for seating arrangements that match the needs of spectators by generating detailed profiles that reflect their interests and preferences. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs spectator data into the generation AI, and the generation AI analyzes the interests and preferences of the spectators to generate profiles.

[0071] The classification unit can classify spectators into different segments based on the generated profiles. For example, the classification unit can classify spectators into different segments such as families, avid fans, and first-time spectators. Based on the characteristics of the spectators, the classification unit classifies them into segments to suggest the optimal seating arrangement. For example, the classification unit can classify families into a segment that places them in an area where children can enjoy themselves. The classification unit can also classify avid fans into a segment that places them in seats closer to the players. Furthermore, the classification unit can classify first-time spectators into a segment that places them in seats that offer a good view. By classifying spectators into different segments, seating arrangements can be made that meet the needs and preferences of the spectators. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input the generated profiles into AI, which can then classify the spectators into different segments.

[0072] The suggestion department can suggest areas where children can enjoy themselves for families and seats closer to the players for avid fans. For example, for families, the suggestion department can suggest seats in areas where children can enjoy themselves and interact with other families. For example, the suggestion department can suggest seats considering the presence of playground equipment, children's events, and family-friendly services. Also, for avid fans, the suggestion department can suggest seats closer to the players where they can cheer together with other fans. For example, the suggestion department can suggest seats considering fieldside, near the bench, or in specific zones. Furthermore, the suggestion department can suggest seats that are easy to watch for first-time spectators. For example, the suggestion department can suggest seats considering the spectator's viewpoint, sound effects, and ease of access. In this way, the viewing experience is improved by suggesting seating arrangements that meet the needs and preferences of the spectators. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input spectator profiles into AI, and the AI ​​can suggest the optimal seating arrangement.

[0073] The data collection unit can estimate the emotions of the audience and adjust the timing of data collection based on the estimated emotions. For example, if the audience is excited during a match, the data collection unit can collect social media posts in real time to obtain data at the peak of their emotions. For example, the data collection unit can capture the audience's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also send questionnaires to audience members who are relaxed after a match to collect detailed feedback. For example, the data collection unit can record the audience's voice and estimate their emotions using voice analysis technology. The data collection unit can analyze the tone and speed of their voice and calculate an emotion score. The data collection unit can also analyze the ticket purchase history and collect data based on anticipation if the audience has expectations before a match. For example, the data collection unit can collect the audience's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows for the collection of more accurate data by adjusting the timing of data collection according to the audience's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input audience emotion data into an AI, which can then estimate the emotions and adjust the timing of data collection.

[0074] The data collection unit can analyze spectators' past viewing history and select the optimal data collection method. For example, based on data from matches spectators have watched in the past, the data collection unit can identify which matches were particularly interesting and prioritize the collection of data related to those matches. For example, the data collection unit can analyze responses to surveys spectators have taken in the past, identify which questions provided the most useful information, and reuse similar questions. The data collection unit can also analyze what spectators have posted on social media in the past and prioritize the collection of posts containing specific hashtags or keywords. This allows for more effective data collection by analyzing spectators' past viewing history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input spectators' past viewing history into AI, which can then select the optimal data collection method.

[0075] The data collection unit can filter data based on the audience's current interests and preferences during data collection. For example, if an audience member is interested in a particular player, the data collection unit will prioritize collecting data related to that player. For example, if an audience member is interested in the highlights of a particular match, the data collection unit will prioritize collecting data related to the highlights of that match. The data collection unit can also prioritize collecting data related to a particular event or promotion if an audience member is interested in that event or promotion. This allows for the collection of more relevant data by filtering the data based on the audience's current interests and preferences. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the audience's current interests and preferences into the AI, which can then filter the data.

[0076] The data collection unit can estimate the emotions of the audience and determine the priority of data to collect based on the estimated emotions. For example, if the audience is excited during a match, the data collection unit will prioritize collecting data related to that excitement. For instance, the data collection unit can capture the audience's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also prioritize collecting data related to the relaxation of the audience after a match. For example, the data collection unit can record the audience's voice and estimate their emotions using voice analysis technology. The data collection unit can analyze the tone and speed of their voice and calculate an emotion score. Furthermore, if the audience has expectations before a match, the data collection unit can also prioritize collecting data related to those expectations. For example, the data collection unit can collect the audience's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows for the priority of data collection based on the audience's emotions, enabling the collection of more important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input audience emotion data into an AI, which can then estimate emotions and determine the priority of the data.

[0077] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of spectators during data collection. For example, if a spectator is in a specific area of ​​the stadium, the data collection unit will prioritize the collection of data related to that area. For example, if a spectator is from a specific city or region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if a spectator is from a specific country, the data collection unit can prioritize the collection of data related to that country. This allows for the collection of more relevant data by considering the geographical location information of spectators. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of spectators into the AI, which can then prioritize the collection of highly relevant data.

[0078] The data collection unit can analyze the audience's social media activity and collect relevant data during data collection. For example, if the audience uses a specific hashtag, the data collection unit will prioritize collecting data related to that hashtag. For example, if the audience follows a specific account, the data collection unit will prioritize collecting data related to that account. The data collection unit can also prioritize collecting data related to a post if the audience shares a specific post. This allows for the collection of more relevant data by analyzing the audience's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the audience's social media activity into an AI, which can then collect the relevant data.

[0079] The generation unit can estimate the audience's emotions and adjust the way the profile is represented based on the estimated emotions. For example, if the audience is excited, the generation unit can generate a visually stimulating profile. For instance, the generation unit can capture the audience's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can calculate an emotion score based on changes in facial expressions. The generation unit can also generate a profile with a calm design if the audience is relaxed. For example, the generation unit can record the audience's voice and estimate their emotions using voice analysis technology. The generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also generate a simple and highly visual profile if the audience is tense. For example, the generation unit can collect the audience's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The generation unit can calculate an emotion score based on fluctuations in heart rate. This allows for the generation of more appropriate profiles by adjusting the way the profile is represented based on the audience's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generative AI. For example, the generation unit can input audience emotion data into the generative AI, which can then estimate the emotions and adjust how the profile is represented.

[0080] The generation unit can adjust the level of detail in a profile based on the audience's level of interest during profile generation. For example, if an audience member has a strong interest in a particular player, the generation unit can generate a profile that includes detailed information about that player. For example, the generation unit can analyze audience data, extract information about the specific player, and reflect it in the profile. The generation unit can also generate a profile that includes an overview of the entire match if the audience member is interested in the match as a whole. For example, the generation unit can include information about match highlights and important plays in the profile. The generation unit can also generate a profile that includes detailed information about an event if the audience member is interested in a particular event. For example, the generation unit can include information about the event's schedule and participants in the profile. This allows for the generation of more relevant profiles by adjusting the level of detail in the profile based on the audience member's level of interest. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the audience member's level of interest into the generation AI, which can then adjust the level of detail in the profile.

[0081] The generation unit can apply different generation algorithms depending on the spectator category when generating profiles. For example, the generation unit can generate a profile that emphasizes family-friendly information for families. For example, the generation unit can analyze spectator data, extract family-friendly information, and reflect it in the profile. The generation unit can also generate a profile that includes detailed information about players for avid fans. For example, the generation unit can analyze spectator data, extract information about players, and reflect it in the profile. Furthermore, the generation unit can generate a profile that includes basic information for first-time spectators. For example, the generation unit can analyze spectator data and include basic match information and viewing points in the profile. This allows for the generation of more appropriate profiles by applying different generation algorithms depending on the spectator category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the spectator category into the generation AI, and the generation AI can apply different generation algorithms.

[0082] The generation unit can estimate the audience's emotions and adjust the profile length based on the estimated emotions. For example, if the audience is in a hurry, the generation unit will generate a short, concise profile. For example, the generation unit can capture the audience's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can calculate an emotion score based on changes in facial expressions. The generation unit can also generate a longer profile with more detailed explanations if the audience is relaxed. For example, the generation unit can record the audience's voice and estimate their emotions using voice analysis technology. The generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also generate a profile with visually stimulating effects if the audience is excited. For example, the generation unit can collect the audience's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The generation unit can calculate an emotion score based on fluctuations in heart rate. This allows for the generation of more appropriate profiles by adjusting the profile length based on the audience's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generative AI. For example, the generation unit can input audience emotion data into the generative AI, which can then estimate the emotion and adjust the profile length.

[0083] The generation unit can determine the priority of profiles based on the timing of audience data collection when generating profiles. For example, the generation unit can generate profiles based on recently collected audience data. For example, the generation unit analyzes audience data and prioritizes reflecting recent data in the profile. The generation unit can also generate profiles based on past audience data. For example, the generation unit analyzes audience data and reflects past data in the profile. The generation unit can also generate profiles based on data related to specific events or matches. For example, the generation unit analyzes audience data and reflects information related to specific events or matches in the profile. This allows for the generation of more relevant profiles by determining the priority of profiles based on the timing of audience data collection. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the timing of audience data collection into the generation AI, and the generation AI can determine the priority of profiles.

[0084] The generation unit can adjust the order of profiles based on the relevance of the audience members when generating profiles. For example, if an audience member is interested in a particular player, the generation unit will display information about that player first. For example, the generation unit can analyze audience data and place information about the specific player at the beginning of the profile. The generation unit can also display information about a particular match first if an audience member is interested in that match. For example, the generation unit can analyze audience data and place information about the particular match at the beginning of the profile. The generation unit can also display information about an event first if an audience member is interested in that event. For example, the generation unit can analyze audience data and place information about the particular event at the beginning of the profile. By adjusting the order of profiles based on the relevance of the audience members, more relevant profiles can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input audience relevance into a generation AI, and the generation AI can adjust the order of the profiles.

[0085] The classification unit can estimate the audience's emotions and adjust the classification criteria based on the estimated emotions. For example, if the audience is excited, the classification unit can classify them based on their level of excitement. For instance, the classification unit can capture the audience's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The classification unit can calculate an emotion score based on changes in facial expressions. The classification unit can also classify the audience based on their level of relaxation if they are relaxed. For example, the classification unit can record the audience's voice and estimate their emotions using voice analysis technology. The classification unit can analyze the tone and speed of their voice and calculate an emotion score. The classification unit can also classify the audience based on their level of tension if they are nervous. For example, the classification unit can collect the audience's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The classification unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate classification by adjusting the classification criteria based on the audience's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input audience emotion data into an AI, which can then estimate emotions and adjust the classification criteria.

[0086] The classification unit can improve the accuracy of its classification by considering the relationships between audience members. For example, if an audience member is a family, the classification unit can classify the entire family as one group. For example, the classification unit analyzes the audience data, identifies family relationships, and classifies the entire family as one group. The classification unit can also classify if an audience member is a group of friends. For example, the classification unit analyzes the audience data, identifies friendships, and classifies the entire group of friends. Furthermore, if audience members share the same interests, the classification unit can classify them as a group based on those interests. For example, the classification unit analyzes the audience data and classifies audience members with common interests as a group. This allows for more accurate classification by considering the relationships between audience members. Some or all of the above processing in the classification unit may be performed using AI, or it may be performed without AI. For example, the classification unit can input the relationships between audience members into the AI, which can then improve the accuracy of the classification.

[0087] The classification unit can perform classification while considering the attribute information of the audience. For example, the classification unit can classify based on the age of the audience. For example, the classification unit can analyze the audience data and classify the audience by age group. The classification unit can also classify based on the gender of the audience. For example, the classification unit can analyze the audience data and classify the audience by gender. The classification unit can also classify based on the place of residence of the audience. For example, the classification unit can analyze the audience data and classify the audience by place of residence. This allows for more appropriate classification by considering the attribute information of the audience. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input the attribute information of the audience into AI, and the AI ​​can perform the classification.

[0088] The classification unit can estimate the audience's emotions and adjust the order in which the classification results are displayed based on the estimated emotions. For example, if the audience is excited, the classification unit can display the classification results in descending order of excitement level. For example, the classification unit can capture the audience's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The classification unit can calculate an emotion score based on changes in facial expressions. The classification unit can also display the classification results in descending order of relaxation level if the audience is relaxed. For example, the classification unit can record the audience's voice and estimate their emotions using voice analysis technology. The classification unit can analyze the tone and speed of the voice and calculate an emotion score. The classification unit can also display the classification results in descending order of tension level if the audience is tense. For example, the classification unit can collect the audience's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The classification unit can calculate an emotion score based on fluctuations in heart rate. This allows for the provision of more appropriate information by adjusting the display order of classification results based on the audience's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the classification unit may be performed using AI or not. For example, the classification unit can input audience emotion data into an AI, which can then estimate emotions and adjust the display order of the classification results.

[0089] The classification unit can perform classification while considering the geographical distribution of the audience. For example, if the audience comes from a specific region, the classification unit can classify them based on that region. For example, the classification unit can analyze the audience data and classify the audience by specific region. The classification unit can also classify the audience based on a specific city if the audience comes from that city. For example, the classification unit can analyze the audience data and classify the audience by specific city. The classification unit can also classify the audience based on a specific country if the audience comes from that country. For example, the classification unit can analyze the audience data and classify the audience by specific country. This allows for more appropriate classification by considering the geographical distribution of the audience. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input the geographical distribution of the audience into AI, and the AI ​​can perform the classification.

[0090] The classification unit can improve the accuracy of its classification by referring to relevant literature for the audience during the classification process. For example, if the audience has referred to literature about a specific player, the classification unit can classify based on that literature. For example, the classification unit can analyze the audience data and classify based on literature about a specific player. The classification unit can also classify based on literature if the audience has referred to literature about a specific match. For example, the classification unit can analyze the audience data and classify based on literature about a specific match. The classification unit can also classify based on literature if the audience has referred to literature about a specific event. For example, the classification unit can analyze the audience data and classify based on literature about a specific event. This allows for more accurate classification by referring to relevant literature for the audience. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input relevant literature for the audience into the AI, which can then improve the accuracy of the classification.

[0091] The proposal unit can estimate the audience's emotions and adjust the presentation of the proposal based on those emotions. For example, if the audience is excited, the proposal unit can make visually stimulating proposals. For instance, it can capture the audience's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The proposal unit can calculate an emotion score based on changes in facial expressions. Furthermore, if the audience is relaxed, the proposal unit can make calming design proposals. For example, it can record the audience's voice and estimate their emotions using voice analysis technology. The proposal unit can analyze the tone and speed of their voice and calculate an emotion score. Also, if the audience is tense, the proposal unit can make simple and highly visible proposals. For example, it can collect the audience's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The proposal unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate proposals by adjusting the presentation based on the audience's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal-generating AI. Some or all of the processing described above in the proposal section may be performed using AI or not. For example, the proposal section may input audience emotion data into the AI, which may estimate the emotions and adjust the way the proposal is presented.

[0092] The suggestion function can adjust the level of detail in its suggestions based on the audience's level of interest. For example, if an audience member has a strong interest in a particular player, the suggestion function can provide a suggestion that includes detailed information about that player. For instance, the suggestion function can analyze audience data, extract information about the specific player, and incorporate it into the suggestion. Alternatively, if an audience member is interested in the entire match, the suggestion function can provide a suggestion that includes an overview of the entire match. For example, the suggestion function can analyze audience data and include information about match highlights and important plays in the suggestion. Furthermore, if an audience member is interested in a particular event, the suggestion function can provide a suggestion that includes detailed information about that event. For example, the suggestion function can analyze audience data and include information about the event's schedule and participants in the suggestion. This allows for more relevant suggestions by adjusting the level of detail based on the audience's level of interest. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input the audience's level of interest into the AI, which can then adjust the level of detail in the suggestion.

[0093] The suggestion unit can apply different suggestion algorithms depending on the audience category when making suggestions. For example, the suggestion unit can make suggestions that emphasize family-friendly information to families. For example, the suggestion unit can analyze audience data, extract family-friendly information, and incorporate it into the suggestions. The suggestion unit can also make suggestions that include detailed information about players to avid fans. For example, the suggestion unit can analyze audience data, extract information about players, and incorporate it into the suggestions. Furthermore, the suggestion unit can make suggestions that include basic information to first-time spectators. For example, the suggestion unit can analyze audience data and include basic match information and viewing points in the suggestions. This allows for more appropriate suggestions by applying different suggestion algorithms depending on the audience category. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input audience categories into the AI, and the AI ​​can apply different suggestion algorithms.

[0094] The suggestion unit can estimate the audience's emotions and adjust the length of the suggestion based on those emotions. For example, if the audience is in a hurry, the suggestion unit will make a short, concise suggestion. For instance, the suggestion unit can capture the audience's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can calculate an emotion score based on changes in facial expressions. Conversely, if the audience is relaxed, the suggestion unit can make a longer suggestion that includes detailed explanations. For example, the suggestion unit can record the audience's voice and estimate their emotions using voice analysis technology. The suggestion unit can analyze the tone and speed of their voice and calculate an emotion score. Furthermore, if the audience is excited, the suggestion unit can make a suggestion with visually stimulating effects. For example, the suggestion unit can collect the audience's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The suggestion unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the audience's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input audience emotion data into an AI, which can then estimate the emotion and adjust the length of the proposal.

[0095] The proposal department can prioritize proposals based on the timing of audience data collection. For example, the proposal department can make proposals based on recently collected audience data. For example, the proposal department can analyze audience data and prioritize the incorporation of recent data into proposals. The proposal department can also make proposals based on past audience data. For example, the proposal department can analyze audience data and incorporate past data into proposals. The proposal department can also make proposals based on data related to specific events or matches. For example, the proposal department can analyze audience data and incorporate information related to specific events or matches into proposals. This allows for more relevant proposals by prioritizing proposals based on the timing of audience data collection. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the timing of audience data collection into AI, and the AI ​​can determine the priority of proposals.

[0096] The suggestion unit can adjust the order of suggestions based on the audience's relevance. For example, if an audience member is interested in a particular player, the suggestion unit will suggest information about that player first. For example, the suggestion unit can analyze audience data and place information about the specific player at the beginning of the suggestions. The suggestion unit can also suggest information about a particular match first if an audience member is interested in that match. For example, the suggestion unit can analyze audience data and place information about the specific match at the beginning of the suggestions. The suggestion unit can also suggest information about an event first if an audience member is interested in that event. For example, the suggestion unit can analyze audience data and place information about the specific event at the beginning of the suggestions. By adjusting the order of suggestions based on the audience's relevance, more relevant suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input audience relevance into AI, and the AI ​​can adjust the order of suggestions.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The spectator seating arrangement system can monitor the health status of spectators and adjust seating arrangements based on their health. For example, the data collection unit can collect biometric data such as spectators' heart rate and blood pressure. The data generation unit analyzes the collected biometric data and evaluates the health status of the spectators. The classification unit classifies spectators into different segments based on their health status. For example, it can suggest areas with many stairs to spectators in good health and areas with easy access to seating to spectators in unstable health. This enables seating arrangements tailored to the health status of spectators, improving the viewing experience.

[0099] The seating arrangement system can analyze spectators' past purchase history and suggest seating arrangements based on their purchasing trends for specific products. For example, the collection unit collects spectators' past purchase history of food, beverages, and merchandise. The generation unit analyzes the collected data to identify spectators' purchasing trends. The classification unit categorizes spectators into different segments based on their purchasing trends. For example, it can suggest areas where certain food and beverages are served to spectators who prefer specific merchandise, and suggest seats near merchandise booths to spectators who prefer certain goods. This enables seating arrangements tailored to spectators' purchasing trends, improving the viewing experience.

[0100] The spectator seating arrangement system can estimate the emotions of spectators, evaluate the compatibility between spectators based on the estimated emotions, and place compatible spectators near each other. For example, the collection unit collects spectators' social media posts and audio data and estimates their emotions using an emotion estimation algorithm. The generation unit generates spectator emotion profiles based on the estimated emotion data. The classification unit evaluates the compatibility between spectators based on the emotion profiles and can place compatible spectators in the same area. This promotes interaction among spectators and improves the viewing experience.

[0101] The spectator seating arrangement system can propose events to promote interaction among spectators based on their hobbies and interests. For example, the data collection unit collects spectator survey results and social media posts to identify their hobbies and interests. The generation unit analyzes the collected data and generates profiles based on the spectators' hobbies and interests. The classification unit categorizes spectators into different segments based on their hobbies and interests. The proposal unit can then propose events to promote interaction among spectators based on the classified segments. This promotes interaction among spectators and improves the viewing experience.

[0102] The spectator seating arrangement system can estimate the emotions of spectators, evaluate their stress levels based on the estimated emotions, and propose seating arrangements to reduce stress. For example, the collection unit collects biometric and audio data from spectators and estimates their emotions using an emotion estimation algorithm. The generation unit evaluates the stress levels of spectators based on the estimated emotion data. The classification unit classifies spectators into different segments based on their stress levels. The proposal unit can suggest relaxing areas for spectators with high stress levels and active areas for spectators with low stress levels. This reduces spectator stress and improves the viewing experience.

[0103] The seating arrangement system can analyze spectators' past viewing history and suggest seating arrangements based on their interest in specific matches or events. For example, the collection unit collects spectators' past viewing history and identifies which matches or events they attended. The generation unit analyzes the collected data to identify spectators' interests. The classification unit categorizes spectators into different segments based on their interests. The suggestion unit can suggest areas related to specific matches or events to spectators who are interested in those matches or events. This results in seating arrangements tailored to spectators' interests, improving the viewing experience.

[0104] The spectator seating arrangement system can estimate the emotions of spectators, evaluate their energy levels based on those estimated emotions, and propose seating arrangements appropriate to their energy levels. For example, the data collection unit collects biometric and audio data from spectators and estimates their emotions using an emotion estimation algorithm. The generation unit evaluates the energy levels of spectators based on the estimated emotion data. The classification unit classifies spectators into different segments based on their energy levels. The proposal unit can suggest active areas for spectators with high energy levels and relaxing areas for spectators with low energy levels. This enables seating arrangements tailored to the energy levels of spectators, improving the viewing experience.

[0105] The seating arrangement system can seat spectators from the same region near each other based on their geographical origin. For example, the collection unit collects spectator address data and identifies their region of origin. The generation unit analyzes the collected data to identify the spectators' place of origin. The classification unit categorizes spectators into different segments based on their place of origin. The proposal unit can promote interaction among spectators by seating them near each other. This promotes interaction among spectators and improves the viewing experience.

[0106] The spectator seating arrangement system can estimate spectators' emotions, evaluate their satisfaction based on those emotions, and propose seating arrangements to improve satisfaction. For example, the collection unit collects spectators' social media posts and audio data and estimates their emotions using an emotion estimation algorithm. The generation unit evaluates spectators' satisfaction based on the estimated emotion data. The classification unit classifies spectators into different segments based on their satisfaction levels. The proposal unit can suggest better seats to spectators with low satisfaction levels and maintain the current seats for spectators with high satisfaction levels. This improves spectator satisfaction and enhances the viewing experience.

[0107] The spectator seating arrangement system can analyze past spectator feedback and propose seating arrangements based on that feedback. For example, the collection unit collects past survey results and social media posts from spectators and identifies feedback. The generation unit analyzes the collected data and identifies spectator feedback. The classification unit classifies spectators into different segments based on the feedback. The proposal unit can propose seating arrangements that meet spectator needs based on past feedback. This results in seating arrangements that respond to spectator feedback and improves the viewing experience.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects audience data. This data includes ticket purchase history, survey results, and social media posts. For example, the data collection unit collects audience attendance history based on ticket purchase history, gathers audience interests and preferences based on survey results, and collects audience impressions and opinions based on social media posts. Step 2: The generation unit uses generation AI to generate audience profiles based on the data collected by the collection unit. The generation unit analyzes the collected data to identify the audience's interests and preferences. For example, it generates profiles that reflect the characteristics of the audience, such as audience members who are interested in a particular player or audience members who are watching the game with their families. Step 3: The classification unit divides the audience into segments based on the profiles generated by the generation unit. The classification unit divides the audience into different segments such as families, avid fans, and first-time spectators. This divides the audience into segments that can be used to suggest the optimal seating arrangement based on their characteristics. Step 4: The proposal department proposes the optimal seating arrangement based on the segments classified by the classification department. For example, it might suggest areas where children can enjoy themselves for families, seats closer to the players for avid fans, and seats that are easy to watch for first-time spectators.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the collection unit, generation unit, classification unit, and proposal unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects audience data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to generate audience profiles. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which classifies the audience into segments based on the generated profiles. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which proposes the optimal seating arrangement based on the classified segments. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, generation unit, classification unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects audience data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to generate audience profiles. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which classifies the audience into segments based on the generated profiles. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which proposes the optimal seating arrangement based on the classified segments. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, generation unit, classification unit, and proposal unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects audience data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to generate audience profiles. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which classifies the audience into segments based on the generated profiles. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which proposes the optimal seating arrangement based on the classified segments. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, generation unit, classification unit, and proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects audience data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 by the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to generate audience profiles. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which classifies the audience into segments based on the generated profiles. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which proposes the optimal seating arrangement based on the classified segments. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A data collection unit that collects audience data, A generation unit generates a profile of the audience based on the data collected by the collection unit, A classification unit that classifies the audience into segments based on the profiles generated by the generation unit, A proposal unit that proposes the optimal seating arrangement based on the segments classified by the classification unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as ticket purchase history, survey results, and social media posts. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the collected data, a detailed profile is generated that reflects the audience's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned classification unit is The audience is then segmented based on the generated profiles. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, For families, we suggest areas where children can have fun, and for avid fans, we suggest seats closer to the players. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the audience's emotions and adjusts the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the past viewing history of spectators and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the audience's current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates audience emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of the audience. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the audience's social media activity is analyzed, and relevant data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the audience's emotions and adjusts how the profile is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating profiles, adjust the level of detail in the profiles based on the audience's level of interest. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating profiles, different generation algorithms are applied depending on the audience category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the audience's emotions and adjusts the profile length based on the estimated audience emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating profiles, prioritize profiles based on when audience data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating profiles, the order of profiles is adjusted based on the relevance of the audience. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned classification unit is The system estimates the audience's emotions and adjusts the classification criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned classification unit is When classifying, consider the relationships between audience members to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned classification unit is When classifying, the attribute information of the audience is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned classification unit is It estimates the audience's emotions and adjusts the order in which the classification results are displayed based on the estimated audience emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned classification unit is When classifying, the geographical distribution of the audience is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned classification unit is During classification, we improve the accuracy of the classification by referring to relevant literature for the audience. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We estimate the audience's emotions and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the audience's level of interest. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the audience category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Estimate the audience's emotions and adjust the length of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making proposals, prioritize them based on when audience data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the audience. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects audience data, A generation unit generates a profile of the audience based on the data collected by the collection unit, A classification unit that classifies the audience into segments based on the profiles generated by the generation unit, A proposal unit that proposes the optimal seating arrangement based on the segments classified by the classification unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as ticket purchase history, survey results, and social media posts. The system according to feature 1.

3. The generating unit is Based on the collected data, a detailed profile is generated that reflects the audience's interests and preferences. The system according to feature 1.

4. The aforementioned classification unit is The audience is then segmented based on the generated profiles. The system according to feature 1.

5. The aforementioned proposal section is, For families, we suggest areas where children can have fun, and for avid fans, we suggest seats closer to the players. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the audience's emotions and adjusts the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the past viewing history of spectators and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the audience's current interests and concerns. The system according to feature 1.

9. The aforementioned collection unit is The system estimates audience emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of the audience. The system according to feature 1.

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