system

Abstract

We provide the system. [Solution] The system includes means for collecting information, and means for obtaining data on matches and related events from multiple databases, A means for analyzing acquired data and generating explanatory information that can be displayed to spectators, A means of sending commentary information to the spectator's device and notifying them via voice or text, A means of receiving interaction from spectators and providing additional information as needed, A method to collect transportation data after the match and suggest the best route home for spectators, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 sports watching, there is a problem that beginners and viewers who are not familiar with the rules cannot fully enjoy the on-site game. This problem is prominent in understanding the rules and situations necessary for watching the game, and in choosing crowded event facilities and return transportation means. Since there is no commentary like a TV broadcast for on-site watching, real-time information provision is scarce and the watching experience is limited.

Means for Solving the Problems

[0005] This invention proposes a system that provides real-time explanations of rules and plays to spectators. Specifically, it uses means to collect information to obtain data on matches and related events from multiple databases, and generates explanatory information for spectators through analysis. It also uses means to transmit the generated explanatory information to the spectator's terminal to support the viewing experience. Furthermore, it accepts interaction from spectators and provides additional information as needed, enabling the provision of information tailored to individual needs.

[0006] "Means for collecting information" refers to a device or method for obtaining data on matches and related events from multiple databases.

[0007] "Means of analysis" refers to technology for generating explanatory information that can be displayed to spectators based on the acquired data.

[0008] "Means for transmitting commentary information" refers to communication technology for transmitting generated commentary information to spectators' devices and notifying them via voice or text.

[0009] "Means for receiving interaction from spectators" refers to an input device or system that receives voice input or tap input from spectators via a terminal and provides necessary additional information based on that input.

[0010] "Means of collecting transportation data" refers to methods for collecting data on local transportation in order to support spectators' travel after the match ends.

[0011] "Methods for suggesting routes home" refers to technologies that analyze transportation data and provide the results to guide spectators to the most suitable route home. [Brief explanation of the drawing]

[0012] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The 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.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system aimed at improving the spectator experience at sports events. Specifically, it provides spectators with information about matches and related events using a device that incorporates a program that enables real-time information gathering, analysis, and notification.

[0034] The server first retrieves information from various databases related to sporting events. This includes match schedules, player data, score information, weather forecasts, and traffic information. The retrieved data is analyzed within the server and generated as commentary information suitable for spectators.

[0035] The terminal operates on the spectator's device and receives information transmitted from the server. This received information is then communicated to the user in either voice or text format. This communication includes information such as the current match status, rule explanations, player profiles, and stadium event information. The terminal can also receive voice input from the spectator and receive corresponding answers from the server, which are then communicated to the user.

[0036] Through this system, users can understand the progress of the match in real time and gain a deeper viewing experience. For example, if a player scores a goal during a match, the server instantly analyzes background information and rules related to that play and sends an explanation to the user's device. By listening to this, users can gain a deeper understanding of the play. In addition, during halftime or between events, users can receive information on food stand congestion and the start time of the next event, allowing them to use their time effectively and enjoy watching the match.

[0037] This system also takes into consideration the spectators' journey home after the match. The server collects the latest data on public transportation and suggests congestion levels and optimal routes home via the terminal. In this way, the system is designed to allow spectators to enjoy the entire viewing experience without stress.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server retrieves match schedules, team information, and player data from official APIs and provided databases before the match begins. It also collects information on transportation status and events within the stadium.

[0041] Step 2:

[0042] The server analyzes the collected data and generates explanatory information about the progress of matches and events. This includes information such as explanations of the match rules, profiles of key players, and past performance records.

[0043] Step 3:

[0044] The terminal receives commentary information sent from the server and prepares to notify the spectator. The terminal prepares the information appropriately according to the notification method (voice or text) selected by the user.

[0045] Step 4:

[0046] Users receive commentary information provided on their devices during the match to understand the flow of the game. Users can ask additional questions via voice input, which are then sent to the server via their devices.

[0047] Step 5:

[0048] The server analyzes the user's questions, generates the necessary answer information, and sends it back to the terminal. This allows the user to obtain more detailed information in real time.

[0049] Step 6:

[0050] The terminal notifies the user of responses sent from the server. It also provides timely notifications about the congestion status inside the stadium and information about upcoming events the user can attend.

[0051] Step 7:

[0052] After the match ends, the server analyzes real-time transportation data to provide users with the best route home. This includes considering congestion and service conditions and presenting several options.

[0053] Step 8:

[0054] Based on the information provided by their device, users can select the most suitable means of transportation home and leave the stadium smoothly.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In watching athletic events, spectators often struggle to understand the progress of the game and information about the players in real time. Furthermore, they face challenges in efficiently obtaining information about transportation options and related events after the game. To address these issues, a system is needed that provides more personalized real-time information and facilitates a smooth return home after the game.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for collecting information about athletic competitions and related events from a large number of information sources using an information processing device, means for analyzing the collected information using machine learning techniques and generating explanatory materials in a format to be provided to spectators, and means for transmitting the explanatory materials to the spectators' display devices and delivering them in audio or text format. This allows spectators to gain a deep understanding of the match in real time and to return home via the most optimal route after the match.

[0060] An "information processing device" is a general term for machines and computer systems that process data quickly and efficiently, and possess the ability to collect, analyze, and generate data.

[0061] "Information sources" refer to databases, APIs, and other external information services that provide data on athletic competitions and related events.

[0062] "Sports competition" refers to sporting events and related matches in which competitors follow established rules.

[0063] "Machine learning techniques" refer to a collection of algorithms and technologies that enable computer systems to learn patterns from data and perform predictions and analyses.

[0064] "Explanatory materials" are information provided in a format that is easy for spectators to understand, and include explanations of match details, player information, tactics, and so on.

[0065] A "display device" is a terminal used by a user to receive information visually or audibly, and includes smartphones, tablets, or computers.

[0066] "Two-way communication" refers to a communication format in which information is exchanged bidirectionally between the system and the user, meaning that responses can be made based on input from the user.

[0067] "Transportation" refers to all modes of transport used by spectators to travel from the venue of the match to their homes or other destinations.

[0068] "Return route" refers to the route spectators take to return home after the event ends, and the aim is to provide optimized route information.

[0069] A description of embodiments for carrying out this invention will be given.

[0070] The server utilizes information processing equipment to collect information about athletic competitions and related events from numerous sources. Specifically, it retrieves JSON data from external services via API requests, converts it into a parseable format, and stores it in a database system. Database software such as PostgreSQL can be used for this database system.

[0071] The terminal functions as a user display device and receives explanatory materials sent from the server. After receiving the materials, it can use a speech generation API as a speech synthesis technology to notify the user of this information in voice or text. It also uses speech recognition technology to accept voice input from the user and analyze its content. Speech recognition uses speech data processing technology to convert the user's questions into text and send it to the server.

[0072] Users can receive various information in real time while watching a match, allowing them to understand the game more deeply. Furthermore, after the match, they can efficiently return home based on transportation information provided by their device. For example, if a user voice-inputs "Tell me the information for the next match" during a soccer game, the server searches relevant data and sends the next match schedule to the device. The device then informs the user via its voice output function.

[0073] An example of a prompt to a generated AI model is, "Please tell me how to build a real-time analysis system for sports viewing and notify spectators of the information." Using this prompt, the necessary information can be obtained from the AI ​​model, which can then be used to further enhance and improve the system.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server collects data related to athletic competitions from various sources. Input data comes from API endpoints and requests from databases. Specifically, the server uses the Python requests library to execute HTTP requests, retrieving match schedules and player information in JSON format. This data is then integrated and stored in the database.

[0077] Step 2:

[0078] The server analyzes the collected data and generates explanatory materials. The input is raw data stored in a database. The server uses a machine learning model to analyze the data and generate information valuable to viewers. Specifically, it uses the Python Pandas library to organize the data and a generative AI model to gain insights into match statistics and player performance.

[0079] Step 3:

[0080] The server sends the generated explanatory materials to the terminal. The input is the analyzed explanatory materials, and the output is the information notified to the end user. Specifically, the server sends text or audio information to the terminal's application via a RESTful API.

[0081] Step 4:

[0082] The terminal notifies the user of the information it receives. The input is explanatory material sent from the server, and the output is a visual or auditory notification to the user. The terminal either converts the text information into speech using a speech synthesis API or displays the text information on the screen.

[0083] Step 5:

[0084] The user provides voice input to the terminal and obtains additional information from the server. The input consists of the user's voice instructions. The terminal uses speech recognition technology to convert the voice into text and sends that text to the server. Specifically, it uses voice data processing technology to analyze the instructions and issue queries to the server.

[0085] Step 6:

[0086] The server provides the terminal with the necessary information in response to the user's request. Input is the text instruction received from the terminal, and output is the requested detailed information. The server retrieves the relevant data from the database and prepares to send it to the terminal.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] The spectator experience at sporting events needs further improvement to enable them to instantly understand the game situation and player information, and to enjoy the entire event. Furthermore, providing efficient and stress-free information regarding transportation during and after matches remains a challenge.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes a device for acquiring information, means for collecting information about the competition and related activities from multiple information sources, means for analyzing the collected information and generating explanatory information that can be displayed to spectators, and means for transmitting detailed information about the match in real time via voice to provide spectators with a deeper understanding. This makes it possible for spectators to obtain the necessary information in real time, to understand the progress of the match more deeply, and to improve the overall viewing experience, including travel after the match.

[0092] A "device for acquiring information" is a device for collecting information about competitions and related activities from various sources.

[0093] "Means of analyzing collected information" refers to technical methods that analyze acquired information and generate useful explanatory information for viewers.

[0094] A "spectator's device" is a terminal used to receive and display commentary information while watching a sporting event, and has the function of notifying users via voice or text.

[0095] "Means for accepting dialogue" refers to system functions that accept voice input and instructions from viewers and provide additional information as needed.

[0096] "Means of collecting transportation information" refers to technology that collects information on public transportation after the match and suggests the best route home to spectators.

[0097] "A means of transmitting detailed information about the match in real time via audio" refers to a technology that instantly analyzes information about the competition and players during the match and transmits it via audio to provide viewers with a deeper understanding.

[0098] In order to implement this invention, it is necessary to build a system that enhances the viewer experience. The system mainly consists of a server and viewer devices (terminals).

[0099] The server includes an information acquisition device designed to collect information about the competition and related activities. This device collects real-time data from numerous sources, analyzes the collected data, and converts it into commentary. It also uses speech recognition to analyze spectator voice input and prepare appropriate responses. The server uses communication technology to transmit the generated commentary information to the spectator's device. Specific technologies that may be used include speech recognition software such as Google® Speech-to-Text API and speech synthesis software such as Amazon Polly.

[0100] The spectator's device has the ability to receive commentary information transmitted from the server and notify them via voice or text. Through this device, spectators can receive detailed commentary in real time about the progress of the match, player information, and traffic information. In this way, the device functions as an interactive viewing tool for spectators.

[0101] For example, if a spectator watching a baseball game wants to know more about a specific player's home run, they can simply say, "Tell me more about the batter who hit this home run," and the system will instantly analyze the player's past performance and profile information and provide a voice explanation. Furthermore, after the game, the system will assess traffic conditions on the return journey and, by asking, "What are the traffic conditions like on my way home today?", it will suggest the optimal route home based on the latest traffic information.

[0102] This system can efficiently provide the information that spectators request using a generation AI model, and instructions can be given to the system using prompts such as the following: "Please tell me how the spectator assistant robot provides the information that users want to enjoy watching a baseball game. Specifically, please show an example of how it would respond when a user requests detailed information about a batter's home run."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server collects information about sporting events from various sources. Inputs include match databases and related activity information, while output is a collection of raw data. This process involves retrieving match schedules, player information, weather forecasts, and other data from external databases using APIs.

[0106] Step 2:

[0107] The server analyzes the collected raw data and generates useful commentary information for viewers. The input is the raw data obtained in step 1, and the output is the processed commentary information. The server uses a data analysis algorithm to extract match trends and player performance, and converts them into information that can be explained in audio or text format.

[0108] Step 3:

[0109] The server transmits the explanatory information obtained through analysis to the viewer's terminal. The input is the explanatory information generated in step 2, and the output is the data transmitted to the terminal. In this process, information is transmitted to the terminal using a communication protocol, and preparations are made for display and notification on the terminal.

[0110] Step 4:

[0111] The terminal notifies the user of information received from the server via voice or text. The input is the data sent in step 3, and the output is the voice or text notification to the user. The terminal uses GUI components and speech synthesis technology to present the information to the viewer in an appropriate format.

[0112] Step 5:

[0113] The user sends interactive instructions and questions to the server via a terminal. Input is either the user's voice or text, and output is a query to the server. The terminal uses speech recognition software to understand the input and manages the process of sending the data to the server.

[0114] Step 6:

[0115] The server processes and provides additional information based on user interaction. The input is the query received in step 5, and the output is the answer to the user. The server utilizes a generative AI model to generate appropriate information and send it back to the terminal.

[0116] Step 7:

[0117] After the match ends, the server collects the latest transportation information and suggests the best route home to the user. The input is a traffic information database, and the output is route information sent to the terminal. The server analyzes the necessary information, calculates an efficient route, and sends the suggested information to the terminal.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] This invention is a system aimed at highly individualizing the spectator experience during sports viewing. The system incorporates an emotion engine that recognizes the spectator's emotions in real time and adjusts the information provided based on the results. The emotion engine includes algorithms for evaluating the spectator's emotional state from their facial expressions, voice tone, and feedback.

[0120] The server manages a database for collecting and analyzing information about sporting events and providing it to viewers. The server continuously monitors the progress of the match and player performance data, analyzing this data to generate commentary. The server also receives emotional data and adjusts the commentary and notification frequency according to the viewers' emotions. For example, if viewers are highly excited, the server reduces detailed commentary and focuses only on key highlights. Conversely, if viewers are bored, it provides additional information and entertainment elements.

[0121] The terminal is installed on the spectator's device and receives information transmitted from the server in real time. The emotion engine is built into the terminal and analyzes the spectator's voice and nonverbal feedback to evaluate their emotional state. Based on the emotional state, the terminal sends notifications via voice or text. If the user requests more detailed information while watching, they can ask a question using voice input, and the result is sent to the server for additional information.

[0122] This system allows users to make the viewing experience more personal. For example, if a user wants to relax during a tense moment, the emotion engine detects this and provides relaxing music or reassuring comments to the device. After the match ends, the system also takes emotional data into account and suggests the least stressful route home, improving the overall viewing experience.

[0123] In this way, the system recognizes the emotional state of the spectator and dynamically adjusts the commentary and information provided based on that state, thereby providing a personalized sports viewing experience.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server collects match information, player data, and event schedules from official APIs and existing databases before the start of matches and events. This ensures that the underlying data can be updated in real time.

[0127] Step 2:

[0128] The server analyzes the collected information and generates explanatory information about important events and rules during the match. This includes details about events such as goals and fouls.

[0129] Step 3:

[0130] The terminal, on the user's device, utilizes an emotion engine to begin analyzing the spectators' facial expressions and voice tone. This allows for real-time understanding of the spectators' emotional state.

[0131] Step 4:

[0132] The server receives emotion data sent from the terminal and dynamically adjusts the content of commentary and notifications based on the viewer's emotions. For example, if the viewer is highly agitated, the amount of information is reduced and the content is switched to a more relaxing one.

[0133] Step 5:

[0134] The device receives pre-arranged explanatory information sent from the server and notifies the user via voice or text. The notification method is optimized according to the user's emotional state.

[0135] Step 6:

[0136] Users can ask questions using voice input as needed, and these questions are sent from the terminal to the server. The server analyzes the questions, generates relevant information, and sends it back.

[0137] Step 7:

[0138] After the match ends, the server collects transportation information again and analyzes the optimal return route, taking into account spectator sentiment data. The terminal provides this information to the user, helping them to travel smoothly while avoiding congestion.

[0139] Step 8:

[0140] Users can choose their mode of transportation home based on the information provided by their device, allowing them to comfortably conclude their viewing experience.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] The spectator experience during sports events is not personalized due to the conventional, uniform information provided, making it difficult to offer information that responds to spectators' emotions and interests. Furthermore, there is insufficient information provided to ensure that spectators can return home comfortably after the event.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes means for analyzing the spectator's facial expressions, voice tone, and feedback, which are equipped with emotion recognition capabilities; means for dynamically adjusting the information provided according to the spectator's emotional state based on the analysis results, and generating commentary or entertainment elements; and means including a prompt sentence function that utilizes a generative AI model to provide detailed information based on voice input from the spectator. This enables spectators to receive personalized information according to their emotional state, improving the viewing experience and allowing them to plan a more comfortable route home.

[0146] "Emotion recognition function" is a technology that analyzes the facial expressions, voice tone, and feedback of spectators to evaluate their emotional state.

[0147] "Dynamic adjustment of information provision" is a function that allows for the instantaneous modification of commentary or entertainment elements in response to the emotional state of the viewers.

[0148] A "generative AI model" is an artificial intelligence-powered model used to provide information in response to diverse inputs, and it can generate detailed information through prompt messages.

[0149] The "prompt message function" is a technology that utilizes instructional messages to derive necessary information and responses based on voice input from spectators, using a generative AI model.

[0150] This invention is a system aimed at recognizing spectators' emotions in real time and personalizing information provision based on those emotions. This system consists of a server, terminals, and users.

[0151] The server manages a database for collecting and providing information related to sporting events to spectators. Specifically, it continuously monitors the progress of the match and player performance data, analyzes this data, and generates commentary information for spectators. Furthermore, the server receives spectator sentiment data and dynamically adjusts the content and frequency of information provided based on this data. The server incorporates a generative AI model that generates prompts that match the spectator's emotional state.

[0152] The terminal is installed on the spectator's device and receives information transmitted from the server in real time. The terminal also features emotion recognition capabilities that analyze the spectator's voice and nonverbal feedback. This allows it to assess the spectator's emotional state and provide informational notifications based on that state, either by voice or text. If the user requests further information during the event, the terminal uses voice input to send this request to the server, which then provides the additional information.

[0153] Through this system, users can enjoy a personalized viewing experience. For example, if a user only wants to see the highlights of a match, the device uses emotion recognition to detect that the user is excited and then provides commentary that focuses only on the most important information. Furthermore, when the user wants to relax, relaxation music or reassuring comments are automatically played.

[0154] As a concrete example, if a user feels nervous while watching a basketball championship game, the system will detect this nervousness using its emotion recognition function and instruct the device to provide relaxing music. A concrete example of a prompt that utilizes a generative AI model would be, "What kind of music would you recommend when the user is feeling nervous?" In this way, the system provides information that responds to the viewer's emotional changes.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The server collects information about sporting events from various sensors and data feeds. It takes data on the progress of matches and player performance as input and stores this information in a database. Using data analysis algorithms, it generates match highlights and important event information, which are then stored in the database. This process forms the basis for the information displayed to spectators.

[0158] Step 2:

[0159] The terminal receives data transmitted from the server in real time. The input data includes match commentary and information about spectators from the server. The emotion engine uses camera and microphone data as input to recognize the spectators' facial expressions and voice tone. This data is processed by an analysis algorithm to evaluate the spectators' emotional state in real time. As a result, information notifications optimized for each spectator become possible.

[0160] Step 3:

[0161] The server receives emotional data transmitted from the terminal and adjusts the content and frequency of existing commentary information. It uses a generative AI model that receives the viewer's emotional state, excitement level, attention level, etc., as input and generates prompt sentences based on this data. Through data processing, it dynamically creates personalized commentary and notifications tailored to the viewer's situation. This operation is performed to make the viewing experience more personal and enriching.

[0162] Step 4:

[0163] When a user inputs a question by voice through the device, the device performs speech recognition based on that audio data. The input audio data is converted into text format and sent to the server. Based on that content, the server generates appropriate answers and additional information through a generative AI model and sends them to the device. Through this process, the user can instantly obtain the information they want.

[0164] Step 5:

[0165] After the game ends, the device uses the user's emotional data and current location information to suggest a less stressful route home. It works in conjunction with traffic data, executing a route selection algorithm that considers the input transportation conditions and road congestion. The suggested route is notified to the device, helping the user to return home comfortably.

[0166] This series of processing steps allows spectators to receive information appropriate to the situation at hand, improving the overall viewing experience.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] Because the viewer's video viewing experience is generally one-way, it lacks personalized information tailored to the individual viewer's emotional state. Existing video streaming services face the challenge of providing optimal content suggestions and relevant information that reflect viewers' interests and emotions.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes a device for collecting information, means for obtaining information about events and related activities from multiple information sources, means for analyzing the acquired information and generating explanatory information that can be displayed to the viewer, and means for detecting the viewer's emotional state and adjusting the explanatory information and notification frequency based on the results. This enables personalized information provision and content optimization in accordance with the viewer's emotional state.

[0172] "Information-collecting device" refers to equipment or software used to obtain information about events and related activities related to video distribution from a variety of sources.

[0173] "Viewer" refers to individual users who watch content using video streaming services.

[0174] "Explanatory information" refers to explanations and detailed information related to the content displayed to the viewer, and is provided for the purpose of complementing the viewing experience.

[0175] "Dialogue" refers to the interaction in which a system responds to input, inquiries, and reactions from viewers.

[0176] "Transportation" refers to the means of transport or methods of transportation that viewers use to return home after the event ends.

[0177] "Emotional state" refers to the state of emotions inferred from the viewer's facial expressions, voice, and nonverbal feedback.

[0178] "Personalization" refers to adjusting services and content according to the individual characteristics and preferences of viewers, thereby optimizing the content offered.

[0179] This invention is a system that dynamically adjusts the delivery of video content based on the emotional state of the viewer. The server is equipped with a device that collects information about events and related activities from multiple sources. This information is received in real time, and a process is carried out to generate explanatory information for the viewer. In this process, natural language processing technology is used to analyze the collected data and select the information that is most relevant to the viewing experience.

[0180] The viewer's device is equipped with a camera and microphone, which are used to analyze the viewer's facial expressions and voice in real time. Software such as "Vision AI" is used for visual information analysis, and "Speech-to-Text API" is used for voice analysis. This allows the viewer's emotional state (excitement, interest, boredom, tension, etc.) to be evaluated. The device sends this emotional information to a server, which then adjusts the content based on those emotions.

[0181] For example, if a viewer sheds tears while watching an emotionally moving film, the system will suggest similar emotionally moving works and interesting information about the actors, as well as recommend other films and episodes tailored to the viewer's preferences. To achieve this, a pre-configured prompt message is sent to the server stating, "The user appears to be moved by the film they are watching. Please provide information about other emotionally moving works or interviews featuring the same actors." Based on the information in this prompt message, a generative AI model selects relevant content and provides personalized suggestions.

[0182] In this way, the system enables the personalization of content according to the viewer's emotional state, providing a more enriching viewing experience.

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The device uses a camera and microphone to capture the viewer's facial expressions and voice in real time. The input is the viewer's video and audio data, which is analyzed by "Vision AI" and "Speech-to-Text API" to determine the viewer's emotional state. The output is metadata indicating the emotional state.

[0186] Step 2:

[0187] The terminal sends the emotional state metadata obtained in step 1 to the server. Changes in the emotional state are also taken into consideration. The input is the emotional state metadata, and the output is the transmission of metadata to the server.

[0188] Step 3:

[0189] The server collects information about related events and activities based on the received sentiment metadata. The input is sentiment metadata. It collects the necessary data from the information sources and filters the most relevant information through natural language processing. The output is the filtered candidate information.

[0190] Step 4:

[0191] The server uses a generative AI model to generate optimal content and information based on sentiment states and filtered candidate information. The input consists of candidate information and sentiment state metadata; the model then selects the most relevant content and constructs prompt sentences. The output is personalized content suggestions.

[0192] Step 5:

[0193] The server sends personalized content information to the device. The input is the content information from the server, and the device notifies the viewer via voice or text. The output is information provided to the viewer.

[0194] Step 6:

[0195] If a user expresses further interest based on the provided content and information, they will make further inquiries to the server through their device. Input is an interaction request from the viewer, which the server receives and processes / provides additional information. Output is further information or content suggestions.

[0196] Through these steps, dynamic information delivery tailored to the viewer's emotional state can be achieved, enriching the viewing experience.

[0197] 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.

[0198] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0204] 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.

[0205] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0206] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0207] 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.

[0208] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0209] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0210] The 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.

[0211] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0212] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0213] This invention is a system aimed at improving the spectator experience at sports events. Specifically, it provides spectators with information about matches and related events using a device that incorporates a program that enables real-time information gathering, analysis, and notification.

[0214] The server first retrieves information from various databases related to sporting events. This includes match schedules, player data, score information, weather forecasts, and traffic information. The retrieved data is analyzed within the server and generated as commentary information suitable for spectators.

[0215] The terminal operates on the spectator's device and receives information transmitted from the server. This received information is then communicated to the user in either voice or text format. This communication includes information such as the current match status, rule explanations, player profiles, and stadium event information. The terminal can also receive voice input from the spectator and receive corresponding answers from the server, which are then communicated to the user.

[0216] Through this system, users can understand the progress of the match in real time and gain a deeper viewing experience. For example, if a player scores a goal during a match, the server instantly analyzes background information and rules related to that play and sends an explanation to the user's device. By listening to this, users can gain a deeper understanding of the play. In addition, during halftime or between events, users can receive information on food stand congestion and the start time of the next event, allowing them to use their time effectively and enjoy watching the match.

[0217] This system also takes into consideration the spectators' journey home after the match. The server collects the latest data on public transportation and suggests congestion levels and optimal routes home via the terminal. In this way, the system is designed to allow spectators to enjoy the entire viewing experience without stress.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The server retrieves match schedules, team information, and player data from official APIs and provided databases before the match begins. It also collects information on transportation status and events within the stadium.

[0221] Step 2:

[0222] The server analyzes the collected data and generates explanatory information about the progress of matches and events. This includes information such as explanations of the match rules, profiles of key players, and past performance records.

[0223] Step 3:

[0224] The terminal receives commentary information sent from the server and prepares to notify the spectator. The terminal prepares the information appropriately according to the notification method (voice or text) selected by the user.

[0225] Step 4:

[0226] Users receive commentary information provided on their devices during the match to understand the flow of the game. Users can ask additional questions via voice input, which are then sent to the server via their devices.

[0227] Step 5:

[0228] The server analyzes the user's questions, generates the necessary answer information, and sends it back to the terminal. This allows the user to obtain more detailed information in real time.

[0229] Step 6:

[0230] The terminal notifies the user of responses sent from the server. It also provides timely notifications about the congestion status inside the stadium and information about upcoming events the user can attend.

[0231] Step 7:

[0232] After the match ends, the server analyzes real-time transportation data to provide users with the best route home. This includes considering congestion and service conditions and presenting several options.

[0233] Step 8:

[0234] Based on the information provided by their device, users can select the most suitable means of transportation home and leave the stadium smoothly.

[0235] (Example 1)

[0236] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0237] In watching athletic events, spectators often struggle to understand the progress of the game and information about the players in real time. Furthermore, they face challenges in efficiently obtaining information about transportation options and related events after the game. To address these issues, a system is needed that provides more personalized real-time information and facilitates a smooth return home after the game.

[0238] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0239] In this invention, the server includes means for collecting information about athletic competitions and related events from a large number of information sources using an information processing device, means for analyzing the collected information using machine learning techniques and generating explanatory materials in a format to be provided to spectators, and means for transmitting the explanatory materials to the spectators' display devices and delivering them in audio or text format. This allows spectators to gain a deep understanding of the match in real time and to return home via the most optimal route after the match.

[0240] An "information processing device" is a general term for machines and computer systems that process data quickly and efficiently, and possess the ability to collect, analyze, and generate data.

[0241] "Information sources" refer to databases, APIs, and other external information services that provide data on athletic competitions and related events.

[0242] "Sports competition" refers to sporting events and related matches in which competitors follow established rules.

[0243] "Machine learning techniques" refer to a collection of algorithms and technologies that enable computer systems to learn patterns from data and perform predictions and analyses.

[0244] "Explanatory materials" are information provided in a format that is easy for spectators to understand, and include explanations of match details, player information, tactics, and so on.

[0245] A "display device" is a terminal used by a user to receive information visually or audibly, and includes smartphones, tablets, or computers.

[0246] "Two-way communication" refers to a communication format in which information is exchanged bidirectionally between the system and the user, meaning that responses can be made based on input from the user.

[0247] "Transportation" refers to all modes of transport used by spectators to travel from the venue of the match to their homes or other destinations.

[0248] "Return route" refers to the route spectators take to return home after the event ends, and the aim is to provide optimized route information.

[0249] A description of embodiments for carrying out this invention will be given.

[0250] The server utilizes information processing equipment to collect information about athletic competitions and related events from numerous sources. Specifically, it retrieves JSON data from external services via API requests, converts it into a parseable format, and stores it in a database system. Database software such as PostgreSQL can be used for this database system.

[0251] The terminal functions as a user display device and receives explanatory materials sent from the server. After receiving the materials, it can use a speech generation API as a speech synthesis technology to notify the user of this information in voice or text. It also uses speech recognition technology to accept voice input from the user and analyze its content. Speech recognition uses speech data processing technology to convert the user's questions into text and send it to the server.

[0252] Users can receive various information in real time while watching a match, allowing them to understand the game more deeply. Furthermore, after the match, they can efficiently return home based on transportation information provided by their device. For example, if a user voice-inputs "Tell me the information for the next match" during a soccer game, the server searches relevant data and sends the next match schedule to the device. The device then informs the user via its voice output function.

[0253] An example of a prompt to a generated AI model is, "Please tell me how to build a real-time analysis system for sports viewing and notify spectators of the information." Using this prompt, the necessary information can be obtained from the AI ​​model, which can then be used to further enhance and improve the system.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] The server collects data related to athletic competitions from various sources. Input data comes from API endpoints and requests from databases. Specifically, the server uses the Python requests library to execute HTTP requests, retrieving match schedules and player information in JSON format. This data is then integrated and stored in the database.

[0257] Step 2:

[0258] The server analyzes the collected data and generates explanatory materials. The input is raw data stored in a database. The server uses a machine learning model to analyze the data and generate information valuable to viewers. Specifically, it uses the Python Pandas library to organize the data and a generative AI model to gain insights into match statistics and player performance.

[0259] Step 3:

[0260] The server sends the generated explanatory materials to the terminal. The input is the analyzed explanatory materials, and the output is the information notified to the end user. Specifically, the server sends text or audio information to the terminal's application via a RESTful API.

[0261] Step 4:

[0262] The terminal notifies the user of the information it receives. The input is explanatory material sent from the server, and the output is a visual or auditory notification to the user. The terminal either converts the text information into speech using a speech synthesis API or displays the text information on the screen.

[0263] Step 5:

[0264] The user provides voice input to the terminal and obtains additional information from the server. The input consists of the user's voice instructions. The terminal uses speech recognition technology to convert the voice into text and sends that text to the server. Specifically, it uses voice data processing technology to analyze the instructions and issue queries to the server.

[0265] Step 6:

[0266] The server provides the terminal with the necessary information in response to the user's request. Input is the text instruction received from the terminal, and output is the requested detailed information. The server retrieves the relevant data from the database and prepares to send it to the terminal.

[0267] (Application Example 1)

[0268] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0269] The spectator experience at sporting events needs further improvement to enable them to instantly understand the game situation and player information, and to enjoy the entire event. Furthermore, providing efficient and stress-free information regarding transportation during and after matches remains a challenge.

[0270] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0271] In this invention, the server includes a device for acquiring information, means for collecting information about the competition and related activities from multiple information sources, means for analyzing the collected information and generating explanatory information that can be displayed to spectators, and means for transmitting detailed information about the match in real time via voice to provide spectators with a deeper understanding. This makes it possible for spectators to obtain the necessary information in real time, to understand the progress of the match more deeply, and to improve the overall viewing experience, including travel after the match.

[0272] A "device for acquiring information" is a device for collecting information about competitions and related activities from various sources.

[0273] "Means of analyzing collected information" refers to technical methods that analyze acquired information and generate useful explanatory information for viewers.

[0274] A "spectator's device" is a terminal used to receive and display commentary information while watching a sporting event, and has the function of notifying users via voice or text.

[0275] "Means for accepting dialogue" refers to system functions that accept voice input and instructions from viewers and provide additional information as needed.

[0276] "Means of collecting transportation information" refers to technology that collects information on public transportation after the match and suggests the best route home to spectators.

[0277] "A means of transmitting detailed information about the match in real time via audio" refers to a technology that instantly analyzes information about the competition and players during the match and transmits it via audio to provide viewers with a deeper understanding.

[0278] In order to implement this invention, it is necessary to build a system that enhances the viewer experience. The system mainly consists of a server and viewer devices (terminals).

[0279] The server includes an information acquisition device designed to collect information about the competition and related activities. This device collects real-time data from numerous sources, analyzes the collected data, and converts it into commentary. It also uses speech recognition to analyze spectator voice input and prepare appropriate responses. The server uses communication technology to transmit the generated commentary information to the spectator's device. Specific technologies that may be used include speech recognition software such as Google Speech-to-Text API and speech synthesis software such as Amazon Polly.

[0280] The spectator's device has the ability to receive commentary information transmitted from the server and notify them via voice or text. Through this device, spectators can receive detailed commentary in real time about the progress of the match, player information, and traffic information. In this way, the device functions as an interactive viewing tool for spectators.

[0281] For example, when a viewer is watching a baseball game and wants to know about a particular player's home run, if they give an instruction like "Tell me more about the batter who hit this home run" verbally, the system will immediately analyze the player's past performance and profile information and provide an explanation verbally. Also, after the game, by asking "What's the traffic situation on my way home today?" to understand the congestion on the way back, it will propose an optimal route home based on the latest traffic information.

[0282] In this system, it is possible to efficiently provide the information required by the viewer using a generative AI model, and the system can be instructed with prompt sentences like the following. "Please tell me how the viewing assistant robot can provide the information that the user wants to enjoy watching during a baseball game. Specifically, show an example of the response when detailed information about a batter's home run is requested."

[0283] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0284] Step 1:

[0285] The server collects information on sports events from various information sources. The input is game database and information on related activities, and the output is a set of raw data. In this process, operations are performed to obtain the game schedule, player information, weather forecast, etc. from external databases using APIs or the like.

[0286] Step 2:

[0287] The server analyzes the collected raw data and generates explanatory information useful to the viewer. The input is the raw data obtained in Step 1, and the output is the processed explanatory information. The server uses a data analysis algorithm to extract game trends and player performance and convert them into information that can be explained in voice or text form.

[0288] <0​​​​The server transmits the explanatory information obtained through analysis to the viewer's terminal. The input is the explanatory information generated in step 2, and the output is the data transmitted to the terminal. In this process, information is transmitted to the terminal using a communication protocol, and preparations are made for display and notification on the terminal.

[0290] Step 4:

[0291] The terminal notifies the user of information received from the server via voice or text. The input is the data sent in step 3, and the output is the voice or text notification to the user. The terminal uses GUI components and speech synthesis technology to present the information to the viewer in an appropriate format.

[0292] Step 5:

[0293] The user sends interactive instructions and questions to the server via a terminal. Input is either the user's voice or text, and output is a query to the server. The terminal uses speech recognition software to understand the input and manages the process of sending the data to the server.

[0294] Step 6:

[0295] The server processes and provides additional information based on user interaction. The input is the query received in step 5, and the output is the answer to the user. The server utilizes a generative AI model to generate appropriate information and send it back to the terminal.

[0296] Step 7:

[0297] After the match ends, the server collects the latest transportation information and suggests the best route home to the user. The input is a traffic information database, and the output is route information sent to the terminal. The server analyzes the necessary information, calculates an efficient route, and sends the suggested information to the terminal.

[0298] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0299] This invention is a system aimed at highly individualizing the spectator experience during sports viewing. The system incorporates an emotion engine that recognizes the spectator's emotions in real time and adjusts the information provided based on the results. The emotion engine includes algorithms for evaluating the spectator's emotional state from their facial expressions, voice tone, and feedback.

[0300] The server manages a database for collecting and analyzing information about sporting events and providing it to viewers. The server continuously monitors the progress of the match and player performance data, analyzing this data to generate commentary. The server also receives emotional data and adjusts the commentary and notification frequency according to the viewers' emotions. For example, if viewers are highly excited, the server reduces detailed commentary and focuses only on key highlights. Conversely, if viewers are bored, it provides additional information and entertainment elements.

[0301] The terminal is installed on the spectator's device and receives information transmitted from the server in real time. The emotion engine is built into the terminal and analyzes the spectator's voice and nonverbal feedback to evaluate their emotional state. Based on the emotional state, the terminal sends notifications via voice or text. If the user requests more detailed information while watching, they can ask a question using voice input, and the result is sent to the server for additional information.

[0302] Users can make the viewing experience more personalized through this system. For example, if a user hopes to relax in a tense situation, the emotion engine will detect this and provide the terminal with music that has a relaxing effect or comments that give a sense of security. Also, after the game ends, considering the emotion data, the least stressful route home will be proposed, improving the overall viewing experience.

[0303] In this way, this system provides a personalized sports viewing experience by recognizing the emotional state of the viewer and dynamically adjusting the commentary and information provision based on it.

[0304] The following explains the processing flow.

[0305] Step 1:

[0306] Before the start of a game or event, the server collects game information, player data, and event schedules from official APIs and existing databases. This ensures basic data that can be updated in real time.

[0307] Step 2:

[0308] The server analyzes the collected information and generates commentary information on important events and rules during the game. This also includes details of events such as goals and fouls.

[0309] Step 3:

[0310] The terminal starts facial expression analysis and voice tone analysis of the viewer using the emotion engine on the user's device. This enables real-time grasp of the viewer's emotional state.

[0311] Step 4:

[0312] The server receives the emotion data sent from the terminal and dynamically adjusts the content of the commentary information and notifications based on the viewer's emotions. For example, when the excitement level is high, the amount of information is reduced and it is switched to content that relaxes.

[0313] Step 5:

[0314] The device receives pre-arranged explanatory information sent from the server and notifies the user via voice or text. The notification method is optimized according to the user's emotional state.

[0315] Step 6:

[0316] Users can ask questions using voice input as needed, and these questions are sent from the terminal to the server. The server analyzes the questions, generates relevant information, and sends it back.

[0317] Step 7:

[0318] After the match ends, the server collects transportation information again and analyzes the optimal return route, taking into account spectator sentiment data. The terminal provides this information to the user, helping them to travel smoothly while avoiding congestion.

[0319] Step 8:

[0320] Users can choose their mode of transportation home based on the information provided by their device, allowing them to comfortably conclude their viewing experience.

[0321] (Example 2)

[0322] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0323] The spectator experience during sports events is not personalized due to the conventional, uniform information provided, making it difficult to offer information that responds to spectators' emotions and interests. Furthermore, there is insufficient information provided to ensure that spectators can return home comfortably after the event.

[0324] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0325] In this invention, the server includes means for analyzing the spectator's facial expressions, voice tone, and feedback, which are equipped with emotion recognition capabilities; means for dynamically adjusting the information provided according to the spectator's emotional state based on the analysis results, and generating commentary or entertainment elements; and means including a prompt sentence function that utilizes a generative AI model to provide detailed information based on voice input from the spectator. This enables spectators to receive personalized information according to their emotional state, improving the viewing experience and allowing them to plan a more comfortable route home.

[0326] "Emotion recognition function" is a technology that analyzes the facial expressions, voice tone, and feedback of spectators to evaluate their emotional state.

[0327] "Dynamic adjustment of information provision" is a function that allows for the instantaneous modification of commentary or entertainment elements in response to the emotional state of the viewers.

[0328] A "generative AI model" is an artificial intelligence-powered model used to provide information in response to diverse inputs, and it can generate detailed information through prompt messages.

[0329] The "prompt message function" is a technology that utilizes instructional messages to derive necessary information and responses based on voice input from spectators, using a generative AI model.

[0330] This invention is a system aimed at recognizing spectators' emotions in real time and personalizing information provision based on those emotions. This system consists of a server, terminals, and users.

[0331] The server manages a database for collecting and providing information related to sporting events to spectators. Specifically, it continuously monitors the progress of the match and player performance data, analyzes this data, and generates commentary information for spectators. Furthermore, the server receives spectator sentiment data and dynamically adjusts the content and frequency of information provided based on this data. The server incorporates a generative AI model that generates prompts that match the spectator's emotional state.

[0332] The terminal is installed on the spectator's device and receives information transmitted from the server in real time. The terminal also features emotion recognition capabilities that analyze the spectator's voice and nonverbal feedback. This allows it to assess the spectator's emotional state and provide informational notifications based on that state, either by voice or text. If the user requests further information during the event, the terminal uses voice input to send this request to the server, which then provides the additional information.

[0333] Through this system, users can enjoy a personalized viewing experience. For example, if a user only wants to see the highlights of a match, the device uses emotion recognition to detect that the user is excited and then provides commentary that focuses only on the most important information. Furthermore, when the user wants to relax, relaxation music or reassuring comments are automatically played.

[0334] As a concrete example, if a user feels nervous while watching a basketball championship game, the system will detect this nervousness using its emotion recognition function and instruct the device to provide relaxing music. A concrete example of a prompt that utilizes a generative AI model would be, "What kind of music would you recommend when the user is feeling nervous?" In this way, the system provides information that responds to the viewer's emotional changes.

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The server collects information about sporting events from various sensors and data feeds. It takes data on the progress of matches and player performance as input and stores this information in a database. Using data analysis algorithms, it generates match highlights and important event information, which are then stored in the database. This process forms the basis for the information displayed to spectators.

[0338] Step 2:

[0339] The terminal receives data transmitted from the server in real time. The input data includes match commentary and information about spectators from the server. The emotion engine uses camera and microphone data as input to recognize the spectators' facial expressions and voice tone. This data is processed by an analysis algorithm to evaluate the spectators' emotional state in real time. As a result, information notifications optimized for each spectator become possible.

[0340] Step 3:

[0341] The server receives emotional data transmitted from the terminal and adjusts the content and frequency of existing commentary information. It uses a generative AI model that receives the viewer's emotional state, excitement level, attention level, etc., as input and generates prompt sentences based on this data. Through data processing, it dynamically creates personalized commentary and notifications tailored to the viewer's situation. This operation is performed to make the viewing experience more personal and enriching.

[0342] Step 4:

[0343] When a user inputs a question by voice through the device, the device performs speech recognition based on that audio data. The input audio data is converted into text format and sent to the server. Based on that content, the server generates appropriate answers and additional information through a generative AI model and sends them to the device. Through this process, the user can instantly obtain the information they want.

[0344] Step 5:

[0345] After the game ends, the device uses the user's emotional data and current location information to suggest a less stressful route home. It works in conjunction with traffic data, executing a route selection algorithm that considers the input transportation conditions and road congestion. The suggested route is notified to the device, helping the user to return home comfortably.

[0346] This series of processing steps allows spectators to receive information appropriate to the situation at hand, improving the overall viewing experience.

[0347] (Application Example 2)

[0348] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0349] Because the viewer's video viewing experience is generally one-way, it lacks personalized information tailored to the individual viewer's emotional state. Existing video streaming services face the challenge of providing optimal content suggestions and relevant information that reflect viewers' interests and emotions.

[0350] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0351] In this invention, the server includes a device for collecting information, means for obtaining information about events and related activities from multiple information sources, means for analyzing the acquired information and generating explanatory information that can be displayed to the viewer, and means for detecting the viewer's emotional state and adjusting the explanatory information and notification frequency based on the results. This enables personalized information provision and content optimization in accordance with the viewer's emotional state.

[0352] "Information-collecting device" refers to equipment or software used to obtain information about events and related activities related to video distribution from a variety of sources.

[0353] "Viewer" refers to individual users who watch content using video streaming services.

[0354] "Explanatory information" refers to explanations and detailed information related to the content displayed to the viewer, and is provided for the purpose of complementing the viewing experience.

[0355] "Dialogue" refers to the interaction in which a system responds to input, inquiries, and reactions from viewers.

[0356] "Transportation" refers to the means of transport or methods of transportation that viewers use to return home after the event ends.

[0357] "Emotional state" refers to the state of emotions inferred from the viewer's facial expressions, voice, and nonverbal feedback.

[0358] "Personalization" refers to adjusting services and content according to the individual characteristics and preferences of viewers, thereby optimizing the content offered.

[0359] This invention is a system that dynamically adjusts the delivery of video content based on the emotional state of the viewer. The server is equipped with a device that collects information about events and related activities from multiple sources. This information is received in real time, and a process is carried out to generate explanatory information for the viewer. In this process, natural language processing technology is used to analyze the collected data and select the information that is most relevant to the viewing experience.

[0360] The viewer's device is equipped with a camera and microphone, which are used to analyze the viewer's facial expressions and voice in real time. Software such as "Vision AI" is used for visual information analysis, and "Speech-to-Text API" is used for voice analysis. This allows the viewer's emotional state (excitement, interest, boredom, tension, etc.) to be evaluated. The device sends this emotional information to a server, which then adjusts the content based on those emotions.

[0361] For example, if a viewer sheds tears while watching an emotionally moving film, the system will suggest similar emotionally moving works and interesting information about the actors, as well as recommend other films and episodes tailored to the viewer's preferences. To achieve this, a pre-configured prompt message is sent to the server stating, "The user appears to be moved by the film they are watching. Please provide information about other emotionally moving works or interviews featuring the same actors." Based on the information in this prompt message, a generative AI model selects relevant content and provides personalized suggestions.

[0362] In this way, the system enables the personalization of content according to the viewer's emotional state, providing a more enriching viewing experience.

[0363] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0364] Step 1:

[0365] The device uses a camera and microphone to capture the viewer's facial expressions and voice in real time. The input is the viewer's video and audio data, which is analyzed by "Vision AI" and "Speech-to-Text API" to determine the viewer's emotional state. The output is metadata indicating the emotional state.

[0366] Step 2:

[0367] The terminal sends the emotional state metadata obtained in step 1 to the server. Changes in the emotional state are also taken into consideration. The input is the emotional state metadata, and the output is the transmission of metadata to the server.

[0368] Step 3:

[0369] The server collects information about related events and activities based on the received sentiment metadata. The input is sentiment metadata. It collects the necessary data from the information sources and filters the most relevant information through natural language processing. The output is the filtered candidate information.

[0370] Step 4:

[0371] The server uses a generative AI model to generate optimal content and information based on sentiment states and filtered candidate information. The input consists of candidate information and sentiment state metadata; the model then selects the most relevant content and constructs prompt sentences. The output is personalized content suggestions.

[0372] Step 5:

[0373] The server sends personalized content information to the device. The input is the content information from the server, and the device notifies the viewer via voice or text. The output is information provided to the viewer.

[0374] Step 6:

[0375] If a user expresses further interest based on the provided content and information, they will make further inquiries to the server through their device. Input is an interaction request from the viewer, which the server receives and processes / provides additional information. Output is further information or content suggestions.

[0376] Through these steps, dynamic information delivery tailored to the viewer's emotional state can be achieved, enriching the viewing experience.

[0377] 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.

[0378] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0379] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] 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.

[0383] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0384] 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.

[0385] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0386] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0387] 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.

[0388] 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.

[0389] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0390] The 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.

[0391] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0392] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0393] This invention is a system aimed at improving the spectator experience at sports events. Specifically, it provides spectators with information about matches and related events using a device that incorporates a program that enables real-time information gathering, analysis, and notification.

[0394] The server first retrieves information from various databases related to sporting events. This includes match schedules, player data, score information, weather forecasts, and traffic information. The retrieved data is analyzed within the server and generated as commentary information suitable for spectators.

[0395] The terminal operates on the spectator's device and receives information transmitted from the server. This received information is then communicated to the user in either voice or text format. This communication includes information such as the current match status, rule explanations, player profiles, and stadium event information. The terminal can also receive voice input from the spectator and receive corresponding answers from the server, which are then communicated to the user.

[0396] Through this system, users can understand the progress of the match in real time and gain a deeper viewing experience. For example, if a player scores a goal during a match, the server instantly analyzes background information and rules related to that play and sends an explanation to the user's device. By listening to this, users can gain a deeper understanding of the play. In addition, during halftime or between events, users can receive information on food stand congestion and the start time of the next event, allowing them to use their time effectively and enjoy watching the match.

[0397] This system also takes into consideration the spectators' journey home after the match. The server collects the latest data on public transportation and suggests congestion levels and optimal routes home via the terminal. In this way, the system is designed to allow spectators to enjoy the entire viewing experience without stress.

[0398] The following describes the processing flow.

[0399] Step 1:

[0400] The server retrieves match schedules, team information, and player data from official APIs and provided databases before the match begins. It also collects information on transportation status and events within the stadium.

[0401] Step 2:

[0402] The server analyzes the collected data and generates explanatory information about the progress of matches and events. This includes information such as explanations of the match rules, profiles of key players, and past performance records.

[0403] Step 3:

[0404] The terminal receives commentary information sent from the server and prepares to notify the spectator. The terminal prepares the information appropriately according to the notification method (voice or text) selected by the user.

[0405] Step 4:

[0406] Users receive commentary information provided on their devices during the match to understand the flow of the game. Users can ask additional questions via voice input, which are then sent to the server via their devices.

[0407] Step 5:

[0408] The server analyzes the user's questions, generates the necessary answer information, and sends it back to the terminal. This allows the user to obtain more detailed information in real time.

[0409] Step 6:

[0410] The terminal notifies the user of responses sent from the server. It also provides timely notifications about the congestion status inside the stadium and information about upcoming events the user can attend.

[0411] Step 7:

[0412] After the match ends, the server analyzes real-time transportation data to provide users with the best route home. This includes considering congestion and service conditions and presenting several options.

[0413] Step 8:

[0414] Based on the information provided by their device, users can select the most suitable means of transportation home and leave the stadium smoothly.

[0415] (Example 1)

[0416] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0417] In watching athletic events, spectators often struggle to understand the progress of the game and information about the players in real time. Furthermore, they face challenges in efficiently obtaining information about transportation options and related events after the game. To address these issues, a system is needed that provides more personalized real-time information and facilitates a smooth return home after the game.

[0418] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0419] In this invention, the server includes means for collecting information about athletic competitions and related events from a large number of information sources using an information processing device, means for analyzing the collected information using machine learning techniques and generating explanatory materials in a format to be provided to spectators, and means for transmitting the explanatory materials to the spectators' display devices and delivering them in audio or text format. This allows spectators to gain a deep understanding of the match in real time and to return home via the most optimal route after the match.

[0420] An "information processing device" is a general term for machines and computer systems that process data quickly and efficiently, and possess the ability to collect, analyze, and generate data.

[0421] "Information sources" refer to databases, APIs, and other external information services that provide data on athletic competitions and related events.

[0422] "Sports competition" refers to sporting events and related matches in which competitors follow established rules.

[0423] "Machine learning techniques" refer to a collection of algorithms and technologies that enable computer systems to learn patterns from data and perform predictions and analyses.

[0424] "Explanatory materials" are information provided in a format that is easy for spectators to understand, and include explanations of match details, player information, tactics, and so on.

[0425] A "display device" is a terminal used by a user to receive information visually or audibly, and includes smartphones, tablets, or computers.

[0426] "Two-way communication" refers to a communication format in which information is exchanged bidirectionally between the system and the user, meaning that responses can be made based on input from the user.

[0427] "Transportation" refers to all modes of transport used by spectators to travel from the venue of the match to their homes or other destinations.

[0428] "Return route" refers to the route spectators take to return home after the event ends, and the aim is to provide optimized route information.

[0429] A description of embodiments for carrying out this invention will be given.

[0430] The server utilizes information processing equipment to collect information about athletic competitions and related events from numerous sources. Specifically, it retrieves JSON data from external services via API requests, converts it into a parseable format, and stores it in a database system. Database software such as PostgreSQL can be used for this database system.

[0431] The terminal functions as a user display device and receives explanatory materials sent from the server. After receiving the materials, it can use a speech generation API as a speech synthesis technology to notify the user of this information in voice or text. It also uses speech recognition technology to accept voice input from the user and analyze its content. Speech recognition uses speech data processing technology to convert the user's questions into text and send it to the server.

[0432] Users can receive various information in real time while watching a match, allowing them to understand the game more deeply. Furthermore, after the match, they can efficiently return home based on transportation information provided by their device. For example, if a user voice-inputs "Tell me the information for the next match" during a soccer game, the server searches relevant data and sends the next match schedule to the device. The device then informs the user via its voice output function.

[0433] An example of a prompt to a generated AI model is, "Please tell me how to build a real-time analysis system for sports viewing and notify spectators of the information." Using this prompt, the necessary information can be obtained from the AI ​​model, which can then be used to further enhance and improve the system.

[0434] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0435] Step 1:

[0436] The server collects data related to athletic competitions from various sources. Input data comes from API endpoints and requests from databases. Specifically, the server uses the Python requests library to execute HTTP requests, retrieving match schedules and player information in JSON format. This data is then integrated and stored in the database.

[0437] Step 2:

[0438] The server analyzes the collected data and generates explanatory materials. The input is raw data stored in a database. The server uses a machine learning model to analyze the data and generate information valuable to viewers. Specifically, it uses the Python Pandas library to organize the data and a generative AI model to gain insights into match statistics and player performance.

[0439] Step 3:

[0440] The server sends the generated explanatory materials to the terminal. The input is the analyzed explanatory materials, and the output is the information notified to the end user. Specifically, the server sends text or audio information to the terminal's application via a RESTful API.

[0441] Step 4:

[0442] The terminal notifies the user of the information it receives. The input is explanatory material sent from the server, and the output is a visual or auditory notification to the user. The terminal either converts the text information into speech using a speech synthesis API or displays the text information on the screen.

[0443] Step 5:

[0444] The user provides voice input to the terminal and obtains additional information from the server. The input consists of the user's voice instructions. The terminal uses speech recognition technology to convert the voice into text and sends that text to the server. Specifically, it uses voice data processing technology to analyze the instructions and issue queries to the server.

[0445] Step 6:

[0446] The server provides the terminal with the necessary information in response to the user's request. Input is the text instruction received from the terminal, and output is the requested detailed information. The server retrieves the relevant data from the database and prepares to send it to the terminal.

[0447] (Application Example 1)

[0448] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0449] The spectator experience at sporting events needs further improvement to enable them to instantly understand the game situation and player information, and to enjoy the entire event. Furthermore, providing efficient and stress-free information regarding transportation during and after matches remains a challenge.

[0450] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0451] In this invention, the server includes a device for acquiring information, means for collecting information about the competition and related activities from multiple information sources, means for analyzing the collected information and generating explanatory information that can be displayed to spectators, and means for transmitting detailed information about the match in real time via voice to provide spectators with a deeper understanding. This makes it possible for spectators to obtain the necessary information in real time, to understand the progress of the match more deeply, and to improve the overall viewing experience, including travel after the match.

[0452] A "device for acquiring information" is a device for collecting information about competitions and related activities from various sources.

[0453] "Means of analyzing collected information" refers to technical methods that analyze acquired information and generate useful explanatory information for viewers.

[0454] A "spectator's device" is a terminal used to receive and display commentary information while watching a sporting event, and has the function of notifying users via voice or text.

[0455] "Means for accepting dialogue" refers to system functions that accept voice input and instructions from viewers and provide additional information as needed.

[0456] "Means of collecting transportation information" refers to technology that collects information on public transportation after the match and suggests the best route home to spectators.

[0457] "A means of transmitting detailed information about the match in real time via audio" refers to a technology that instantly analyzes information about the competition and players during the match and transmits it via audio to provide viewers with a deeper understanding.

[0458] In order to implement this invention, it is necessary to build a system that enhances the viewer experience. The system mainly consists of a server and viewer devices (terminals).

[0459] The server includes an information acquisition device designed to collect information about the competition and related activities. This device collects real-time data from numerous sources, analyzes the collected data, and converts it into commentary. It also uses speech recognition to analyze spectator voice input and prepare appropriate responses. The server uses communication technology to transmit the generated commentary information to the spectator's device. Specific technologies that may be used include speech recognition software such as Google Speech-to-Text API and speech synthesis software such as Amazon Polly.

[0460] The spectator's device has the ability to receive commentary information transmitted from the server and notify them via voice or text. Through this device, spectators can receive detailed commentary in real time about the progress of the match, player information, and traffic information. In this way, the device functions as an interactive viewing tool for spectators.

[0461] For example, if a spectator watching a baseball game wants to know more about a specific player's home run, they can simply say, "Tell me more about the batter who hit this home run," and the system will instantly analyze the player's past performance and profile information and provide a voice explanation. Furthermore, after the game, the system will assess traffic conditions on the return journey and, by asking, "What are the traffic conditions like on my way home today?", it will suggest the optimal route home based on the latest traffic information.

[0462] This system can efficiently provide the information that spectators request using a generation AI model, and instructions can be given to the system using prompts such as the following: "Please tell me how the spectator assistant robot provides the information that users want to enjoy watching a baseball game. Specifically, please show an example of how it would respond when a user requests detailed information about a batter's home run."

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] The server collects information about sporting events from various sources. Inputs include match databases and related activity information, while output is a collection of raw data. This process involves retrieving match schedules, player information, weather forecasts, and other data from external databases using APIs.

[0466] Step 2:

[0467] The server analyzes the collected raw data and generates useful commentary information for viewers. The input is the raw data obtained in step 1, and the output is the processed commentary information. The server uses a data analysis algorithm to extract match trends and player performance, and converts them into information that can be explained in audio or text format.

[0468] Step 3:

[0469] The server transmits the explanatory information obtained through analysis to the viewer's terminal. The input is the explanatory information generated in step 2, and the output is the data transmitted to the terminal. In this process, information is transmitted to the terminal using a communication protocol, and preparations are made for display and notification on the terminal.

[0470] Step 4:

[0471] The terminal notifies the user of information received from the server via voice or text. The input is the data sent in step 3, and the output is the voice or text notification to the user. The terminal uses GUI components and speech synthesis technology to present the information to the viewer in an appropriate format.

[0472] Step 5:

[0473] The user sends interactive instructions and questions to the server via a terminal. Input is either the user's voice or text, and output is a query to the server. The terminal uses speech recognition software to understand the input and manages the process of sending the data to the server.

[0474] Step 6:

[0475] The server processes and provides additional information based on user interaction. The input is the query received in step 5, and the output is the answer to the user. The server utilizes a generative AI model to generate appropriate information and send it back to the terminal.

[0476] Step 7:

[0477] After the match ends, the server collects the latest transportation information and suggests the best route home to the user. The input is a traffic information database, and the output is route information sent to the terminal. The server analyzes the necessary information, calculates an efficient route, and sends the suggested information to the terminal.

[0478] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0479] This invention is a system aimed at highly individualizing the spectator experience during sports viewing. The system incorporates an emotion engine that recognizes the spectator's emotions in real time and adjusts the information provided based on the results. The emotion engine includes algorithms for evaluating the spectator's emotional state from their facial expressions, voice tone, and feedback.

[0480] The server manages a database for collecting and analyzing information about sporting events and providing it to viewers. The server continuously monitors the progress of the match and player performance data, analyzing this data to generate commentary. The server also receives emotional data and adjusts the commentary and notification frequency according to the viewers' emotions. For example, if viewers are highly excited, the server reduces detailed commentary and focuses only on key highlights. Conversely, if viewers are bored, it provides additional information and entertainment elements.

[0481] The terminal is installed on the spectator's device and receives information transmitted from the server in real time. The emotion engine is built into the terminal and analyzes the spectator's voice and nonverbal feedback to evaluate their emotional state. Based on the emotional state, the terminal sends notifications via voice or text. If the user requests more detailed information while watching, they can ask a question using voice input, and the result is sent to the server for additional information.

[0482] This system allows users to make the viewing experience more personal. For example, if a user wants to relax during a tense moment, the emotion engine detects this and provides relaxing music or reassuring comments to the device. After the match ends, the system also takes emotional data into account and suggests the least stressful route home, improving the overall viewing experience.

[0483] In this way, the system recognizes the emotional state of the spectator and dynamically adjusts the commentary and information provided based on that state, thereby providing a personalized sports viewing experience.

[0484] The following describes the processing flow.

[0485] Step 1:

[0486] The server collects match information, player data, and event schedules from official APIs and existing databases before the start of matches and events. This ensures that the underlying data can be updated in real time.

[0487] Step 2:

[0488] The server analyzes the collected information and generates explanatory information about important events and rules during the match. This includes details about events such as goals and fouls.

[0489] Step 3:

[0490] The terminal, on the user's device, utilizes an emotion engine to begin analyzing the spectators' facial expressions and voice tone. This allows for real-time understanding of the spectators' emotional state.

[0491] Step 4:

[0492] The server receives emotion data sent from the terminal and dynamically adjusts the content of commentary and notifications based on the viewer's emotions. For example, if the viewer is highly agitated, the amount of information is reduced and the content is switched to a more relaxing one.

[0493] Step 5:

[0494] The device receives pre-arranged explanatory information sent from the server and notifies the user via voice or text. The notification method is optimized according to the user's emotional state.

[0495] Step 6:

[0496] Users can ask questions using voice input as needed, and these questions are sent from the terminal to the server. The server analyzes the questions, generates relevant information, and sends it back.

[0497] Step 7:

[0498] After the match ends, the server collects transportation information again and analyzes the optimal return route, taking into account spectator sentiment data. The terminal provides this information to the user, helping them to travel smoothly while avoiding congestion.

[0499] Step 8:

[0500] Users can choose their mode of transportation home based on the information provided by their device, allowing them to comfortably conclude their viewing experience.

[0501] (Example 2)

[0502] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0503] The spectator experience during sports events is not personalized due to the conventional, uniform information provided, making it difficult to offer information that responds to spectators' emotions and interests. Furthermore, there is insufficient information provided to ensure that spectators can return home comfortably after the event.

[0504] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0505] In this invention, the server includes means for analyzing the spectator's facial expressions, voice tone, and feedback, which are equipped with emotion recognition capabilities; means for dynamically adjusting the information provided according to the spectator's emotional state based on the analysis results, and generating commentary or entertainment elements; and means including a prompt sentence function that utilizes a generative AI model to provide detailed information based on voice input from the spectator. This enables spectators to receive personalized information according to their emotional state, improving the viewing experience and allowing them to plan a more comfortable route home.

[0506] "Emotion recognition function" is a technology that analyzes the facial expressions, voice tone, and feedback of spectators to evaluate their emotional state.

[0507] "Dynamic adjustment of information provision" is a function that allows for the instantaneous modification of commentary or entertainment elements in response to the emotional state of the viewers.

[0508] A "generative AI model" is an artificial intelligence-powered model used to provide information in response to diverse inputs, and it can generate detailed information through prompt messages.

[0509] The "prompt message function" is a technology that utilizes instructional messages to derive necessary information and responses based on voice input from spectators, using a generative AI model.

[0510] This invention is a system aimed at recognizing spectators' emotions in real time and personalizing information provision based on those emotions. This system consists of a server, terminals, and users.

[0511] The server manages a database for collecting and providing information related to sporting events to spectators. Specifically, it continuously monitors the progress of the match and player performance data, analyzes this data, and generates commentary information for spectators. Furthermore, the server receives spectator sentiment data and dynamically adjusts the content and frequency of information provided based on this data. The server incorporates a generative AI model that generates prompts that match the spectator's emotional state.

[0512] The terminal is installed on the spectator's device and receives information transmitted from the server in real time. The terminal also features emotion recognition capabilities that analyze the spectator's voice and nonverbal feedback. This allows it to assess the spectator's emotional state and provide informational notifications based on that state, either by voice or text. If the user requests further information during the event, the terminal uses voice input to send this request to the server, which then provides the additional information.

[0513] Through this system, users can enjoy a personalized viewing experience. For example, if a user only wants to see the highlights of a match, the device uses emotion recognition to detect that the user is excited and then provides commentary that focuses only on the most important information. Furthermore, when the user wants to relax, relaxation music or reassuring comments are automatically played.

[0514] As a concrete example, if a user feels nervous while watching a basketball championship game, the system will detect this nervousness using its emotion recognition function and instruct the device to provide relaxing music. A concrete example of a prompt that utilizes a generative AI model would be, "What kind of music would you recommend when the user is feeling nervous?" In this way, the system provides information that responds to the viewer's emotional changes.

[0515] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0516] Step 1:

[0517] The server collects information about sporting events from various sensors and data feeds. It takes data on the progress of matches and player performance as input and stores this information in a database. Using data analysis algorithms, it generates match highlights and important event information, which are then stored in the database. This process forms the basis for the information displayed to spectators.

[0518] Step 2:

[0519] The terminal receives data transmitted from the server in real time. The input data includes match commentary and information about spectators from the server. The emotion engine uses camera and microphone data as input to recognize the spectators' facial expressions and voice tone. This data is processed by an analysis algorithm to evaluate the spectators' emotional state in real time. As a result, information notifications optimized for each spectator become possible.

[0520] Step 3:

[0521] The server receives emotional data transmitted from the terminal and adjusts the content and frequency of existing commentary information. It uses a generative AI model that receives the viewer's emotional state, excitement level, attention level, etc., as input and generates prompt sentences based on this data. Through data processing, it dynamically creates personalized commentary and notifications tailored to the viewer's situation. This operation is performed to make the viewing experience more personal and enriching.

[0522] Step 4:

[0523] When a user inputs a question by voice through the device, the device performs speech recognition based on that audio data. The input audio data is converted into text format and sent to the server. Based on that content, the server generates appropriate answers and additional information through a generative AI model and sends them to the device. Through this process, the user can instantly obtain the information they want.

[0524] Step 5:

[0525] After the game ends, the device uses the user's emotional data and current location information to suggest a less stressful route home. It works in conjunction with traffic data, executing a route selection algorithm that considers the input transportation conditions and road congestion. The suggested route is notified to the device, helping the user to return home comfortably.

[0526] This series of processing steps allows spectators to receive information appropriate to the situation at hand, improving the overall viewing experience.

[0527] (Application Example 2)

[0528] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0529] Because the viewer's video viewing experience is generally one-way, it lacks personalized information tailored to the individual viewer's emotional state. Existing video streaming services face the challenge of providing optimal content suggestions and relevant information that reflect viewers' interests and emotions.

[0530] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0531] In this invention, the server includes a device for collecting information, means for obtaining information about events and related activities from multiple information sources, means for analyzing the acquired information and generating explanatory information that can be displayed to the viewer, and means for detecting the viewer's emotional state and adjusting the explanatory information and notification frequency based on the results. This enables personalized information provision and content optimization in accordance with the viewer's emotional state.

[0532] "Information-collecting device" refers to equipment or software used to obtain information about events and related activities related to video distribution from a variety of sources.

[0533] "Viewer" refers to individual users who watch content using video streaming services.

[0534] "Explanatory information" refers to explanations and detailed information related to the content displayed to the viewer, and is provided for the purpose of complementing the viewing experience.

[0535] "Dialogue" refers to the interaction in which a system responds to input, inquiries, and reactions from viewers.

[0536] "Transportation" refers to the means of transport or methods of transportation that viewers use to return home after the event ends.

[0537] "Emotional state" refers to the state of emotions inferred from the viewer's facial expressions, voice, and nonverbal feedback.

[0538] "Personalization" refers to adjusting services and content according to the individual characteristics and preferences of viewers, thereby optimizing the content offered.

[0539] This invention is a system that dynamically adjusts the delivery of video content based on the emotional state of the viewer. The server is equipped with a device that collects information about events and related activities from multiple sources. This information is received in real time, and a process is carried out to generate explanatory information for the viewer. In this process, natural language processing technology is used to analyze the collected data and select the information that is most relevant to the viewing experience.

[0540] The viewer's device is equipped with a camera and microphone, which are used to analyze the viewer's facial expressions and voice in real time. Software such as "Vision AI" is used for visual information analysis, and "Speech-to-Text API" is used for voice analysis. This allows the viewer's emotional state (excitement, interest, boredom, tension, etc.) to be evaluated. The device sends this emotional information to a server, which then adjusts the content based on those emotions.

[0541] For example, if a viewer sheds tears while watching an emotionally moving film, the system will suggest similar emotionally moving works and interesting information about the actors, as well as recommend other films and episodes tailored to the viewer's preferences. To achieve this, a pre-configured prompt message is sent to the server stating, "The user appears to be moved by the film they are watching. Please provide information about other emotionally moving works or interviews featuring the same actors." Based on the information in this prompt message, a generative AI model selects relevant content and provides personalized suggestions.

[0542] In this way, the system enables the personalization of content according to the viewer's emotional state, providing a more enriching viewing experience.

[0543] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0544] Step 1:

[0545] The device uses a camera and microphone to capture the viewer's facial expressions and voice in real time. The input is the viewer's video and audio data, which is analyzed by "Vision AI" and "Speech-to-Text API" to determine the viewer's emotional state. The output is metadata indicating the emotional state.

[0546] Step 2:

[0547] The terminal sends the emotional state metadata obtained in step 1 to the server. Changes in the emotional state are also taken into consideration. The input is the emotional state metadata, and the output is the transmission of metadata to the server.

[0548] Step 3:

[0549] The server collects information about related events and activities based on the received sentiment metadata. The input is sentiment metadata. It collects the necessary data from the information sources and filters the most relevant information through natural language processing. The output is the filtered candidate information.

[0550] Step 4:

[0551] The server uses a generative AI model to generate optimal content and information based on sentiment states and filtered candidate information. The input consists of candidate information and sentiment state metadata; the model then selects the most relevant content and constructs prompt sentences. The output is personalized content suggestions.

[0552] Step 5:

[0553] The server sends personalized content information to the device. The input is the content information from the server, and the device notifies the viewer via voice or text. The output is information provided to the viewer.

[0554] Step 6:

[0555] If a user expresses further interest based on the provided content and information, they will make further inquiries to the server through their device. Input is an interaction request from the viewer, which the server receives and processes / provides additional information. Output is further information or content suggestions.

[0556] Through these steps, dynamic information delivery tailored to the viewer's emotional state can be achieved, enriching the viewing experience.

[0557] 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.

[0558] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0559] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] 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.

[0563] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0564] 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.

[0565] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0566] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0567] 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.

[0568] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0569] 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.

[0570] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0571] The 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.

[0572] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0573] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0574] This invention is a system aimed at improving the spectator experience at sports events. Specifically, it provides spectators with information about matches and related events using a device that incorporates a program that enables real-time information gathering, analysis, and notification.

[0575] The server first retrieves information from various databases related to sporting events. This includes match schedules, player data, score information, weather forecasts, and traffic information. The retrieved data is analyzed within the server and generated as commentary information suitable for spectators.

[0576] The terminal operates on the spectator's device and receives information transmitted from the server. This received information is then communicated to the user in either voice or text format. This communication includes information such as the current match status, rule explanations, player profiles, and stadium event information. The terminal can also receive voice input from the spectator and receive corresponding answers from the server, which are then communicated to the user.

[0577] Through this system, users can understand the progress of the match in real time and gain a deeper viewing experience. For example, if a player scores a goal during a match, the server instantly analyzes background information and rules related to that play and sends an explanation to the user's device. By listening to this, users can gain a deeper understanding of the play. In addition, during halftime or between events, users can receive information on food stand congestion and the start time of the next event, allowing them to use their time effectively and enjoy watching the match.

[0578] This system also takes into consideration the spectators' journey home after the match. The server collects the latest data on public transportation and suggests congestion levels and optimal routes home via the terminal. In this way, the system is designed to allow spectators to enjoy the entire viewing experience without stress.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] The server retrieves match schedules, team information, and player data from official APIs and provided databases before the match begins. It also collects information on transportation status and events within the stadium.

[0582] Step 2:

[0583] The server analyzes the collected data and generates explanatory information about the progress of matches and events. This includes information such as explanations of the match rules, profiles of key players, and past performance records.

[0584] Step 3:

[0585] The terminal receives commentary information sent from the server and prepares to notify the spectator. The terminal prepares the information appropriately according to the notification method (voice or text) selected by the user.

[0586] Step 4:

[0587] Users receive commentary information provided on their devices during the match to understand the flow of the game. Users can ask additional questions via voice input, which are then sent to the server via their devices.

[0588] Step 5:

[0589] The server analyzes the user's questions, generates the necessary answer information, and sends it back to the terminal. This allows the user to obtain more detailed information in real time.

[0590] Step 6:

[0591] The terminal notifies the user of responses sent from the server. It also provides timely notifications about the congestion status inside the stadium and information about upcoming events the user can attend.

[0592] Step 7:

[0593] After the match ends, the server analyzes real-time transportation data to provide users with the best route home. This includes considering congestion and service conditions and presenting several options.

[0594] Step 8:

[0595] Based on the information provided by their device, users can select the most suitable means of transportation home and leave the stadium smoothly.

[0596] (Example 1)

[0597] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0598] In watching athletic events, spectators often struggle to understand the progress of the game and information about the players in real time. Furthermore, they face challenges in efficiently obtaining information about transportation options and related events after the game. To address these issues, a system is needed that provides more personalized real-time information and facilitates a smooth return home after the game.

[0599] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0600] In this invention, the server includes means for collecting information about athletic competitions and related events from a large number of information sources using an information processing device, means for analyzing the collected information using machine learning techniques and generating explanatory materials in a format to be provided to spectators, and means for transmitting the explanatory materials to the spectators' display devices and delivering them in audio or text format. This allows spectators to gain a deep understanding of the match in real time and to return home via the most optimal route after the match.

[0601] An "information processing device" is a general term for machines and computer systems that process data quickly and efficiently, and possess the ability to collect, analyze, and generate data.

[0602] "Information sources" refer to databases, APIs, and other external information services that provide data on athletic competitions and related events.

[0603] "Sports competition" refers to sporting events and related matches in which competitors follow established rules.

[0604] "Machine learning techniques" refer to a collection of algorithms and technologies that enable computer systems to learn patterns from data and perform predictions and analyses.

[0605] "Explanatory materials" are information provided in a format that is easy for spectators to understand, and include explanations of match details, player information, tactics, and so on.

[0606] A "display device" is a terminal used by a user to receive information visually or audibly, and includes smartphones, tablets, or computers.

[0607] "Two-way communication" refers to a communication format in which information is exchanged bidirectionally between the system and the user, meaning that responses can be made based on input from the user.

[0608] "Transportation" refers to all modes of transport used by spectators to travel from the venue of the match to their homes or other destinations.

[0609] "Return route" refers to the route spectators take to return home after the event ends, and the aim is to provide optimized route information.

[0610] A description of embodiments for carrying out this invention will be given.

[0611] The server utilizes information processing equipment to collect information about athletic competitions and related events from numerous sources. Specifically, it retrieves JSON data from external services via API requests, converts it into a parseable format, and stores it in a database system. Database software such as PostgreSQL can be used for this database system.

[0612] The terminal functions as a user display device and receives explanatory materials sent from the server. After receiving the materials, it can use a speech generation API as a speech synthesis technology to notify the user of this information in voice or text. It also uses speech recognition technology to accept voice input from the user and analyze its content. Speech recognition uses speech data processing technology to convert the user's questions into text and send it to the server.

[0613] Users can receive various information in real time while watching a match, allowing them to understand the game more deeply. Furthermore, after the match, they can efficiently return home based on transportation information provided by their device. For example, if a user voice-inputs "Tell me the information for the next match" during a soccer game, the server searches relevant data and sends the next match schedule to the device. The device then informs the user via its voice output function.

[0614] An example of a prompt to a generated AI model is, "Please tell me how to build a real-time analysis system for sports viewing and notify spectators of the information." Using this prompt, the necessary information can be obtained from the AI ​​model, which can then be used to further enhance and improve the system.

[0615] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0616] Step 1:

[0617] The server collects data related to athletic competitions from various sources. Input data comes from API endpoints and requests from databases. Specifically, the server uses the Python requests library to execute HTTP requests, retrieving match schedules and player information in JSON format. This data is then integrated and stored in the database.

[0618] Step 2:

[0619] The server analyzes the collected data and generates explanatory materials. The input is raw data stored in a database. The server uses a machine learning model to analyze the data and generate information valuable to viewers. Specifically, it uses the Python Pandas library to organize the data and a generative AI model to gain insights into match statistics and player performance.

[0620] Step 3:

[0621] The server sends the generated explanatory materials to the terminal. The input is the analyzed explanatory materials, and the output is the information notified to the end user. Specifically, the server sends text or audio information to the terminal's application via a RESTful API.

[0622] Step 4:

[0623] The terminal notifies the user of the information it receives. The input is explanatory material sent from the server, and the output is a visual or auditory notification to the user. The terminal either converts the text information into speech using a speech synthesis API or displays the text information on the screen.

[0624] Step 5:

[0625] The user provides voice input to the terminal and obtains additional information from the server. The input consists of the user's voice instructions. The terminal uses speech recognition technology to convert the voice into text and sends that text to the server. Specifically, it uses voice data processing technology to analyze the instructions and issue queries to the server.

[0626] Step 6:

[0627] The server provides the terminal with the necessary information in response to the user's request. Input is the text instruction received from the terminal, and output is the requested detailed information. The server retrieves the relevant data from the database and prepares to send it to the terminal.

[0628] (Application Example 1)

[0629] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] The spectator experience at sporting events needs further improvement to enable them to instantly understand the game situation and player information, and to enjoy the entire event. Furthermore, providing efficient and stress-free information regarding transportation during and after matches remains a challenge.

[0631] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0632] In this invention, the server includes a device for acquiring information, means for collecting information about the competition and related activities from multiple information sources, means for analyzing the collected information and generating explanatory information that can be displayed to spectators, and means for transmitting detailed information about the match in real time via voice to provide spectators with a deeper understanding. This makes it possible for spectators to obtain the necessary information in real time, to understand the progress of the match more deeply, and to improve the overall viewing experience, including travel after the match.

[0633] A "device for acquiring information" is a device for collecting information about competitions and related activities from various sources.

[0634] "Means of analyzing collected information" refers to technical methods that analyze acquired information and generate useful explanatory information for viewers.

[0635] A "spectator's device" is a terminal used to receive and display commentary information while watching a sporting event, and has the function of notifying users via voice or text.

[0636] "Means for accepting dialogue" refers to system functions that accept voice input and instructions from viewers and provide additional information as needed.

[0637] "Means of collecting transportation information" refers to technology that collects information on public transportation after the match and suggests the best route home to spectators.

[0638] "A means of transmitting detailed information about the match in real time via audio" refers to a technology that instantly analyzes information about the competition and players during the match and transmits it via audio to provide viewers with a deeper understanding.

[0639] In order to implement this invention, it is necessary to build a system that enhances the viewer experience. The system mainly consists of a server and viewer devices (terminals).

[0640] The server includes an information acquisition device designed to collect information about the competition and related activities. This device collects real-time data from numerous sources, analyzes the collected data, and converts it into commentary. It also uses speech recognition to analyze spectator voice input and prepare appropriate responses. The server uses communication technology to transmit the generated commentary information to the spectator's device. Specific technologies that may be used include speech recognition software such as Google Speech-to-Text API and speech synthesis software such as Amazon Polly.

[0641] The spectator's device has the ability to receive commentary information transmitted from the server and notify them via voice or text. Through this device, spectators can receive detailed commentary in real time about the progress of the match, player information, and traffic information. In this way, the device functions as an interactive viewing tool for spectators.

[0642] For example, if a spectator watching a baseball game wants to know more about a specific player's home run, they can simply say, "Tell me more about the batter who hit this home run," and the system will instantly analyze the player's past performance and profile information and provide a voice explanation. Furthermore, after the game, the system will assess traffic conditions on the return journey and, by asking, "What are the traffic conditions like on my way home today?", it will suggest the optimal route home based on the latest traffic information.

[0643] This system can efficiently provide the information that spectators request using a generation AI model, and instructions can be given to the system using prompts such as the following: "Please tell me how the spectator assistant robot provides the information that users want to enjoy watching a baseball game. Specifically, please show an example of how it would respond when a user requests detailed information about a batter's home run."

[0644] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0645] Step 1:

[0646] The server collects information about sporting events from various sources. Inputs include match databases and related activity information, while output is a collection of raw data. This process involves retrieving match schedules, player information, weather forecasts, and other data from external databases using APIs.

[0647] Step 2:

[0648] The server analyzes the collected raw data and generates useful commentary information for viewers. The input is the raw data obtained in step 1, and the output is the processed commentary information. The server uses a data analysis algorithm to extract match trends and player performance, and converts them into information that can be explained in audio or text format.

[0649] Step 3:

[0650] The server transmits the explanatory information obtained through analysis to the viewer's terminal. The input is the explanatory information generated in step 2, and the output is the data transmitted to the terminal. In this process, information is transmitted to the terminal using a communication protocol, and preparations are made for display and notification on the terminal.

[0651] Step 4:

[0652] The terminal notifies the user of information received from the server via voice or text. The input is the data sent in step 3, and the output is the voice or text notification to the user. The terminal uses GUI components and speech synthesis technology to present the information to the viewer in an appropriate format.

[0653] Step 5:

[0654] The user sends interactive instructions and questions to the server via a terminal. Input is either the user's voice or text, and output is a query to the server. The terminal uses speech recognition software to understand the input and manages the process of sending the data to the server.

[0655] Step 6:

[0656] The server processes and provides additional information based on user interaction. The input is the query received in step 5, and the output is the answer to the user. The server utilizes a generative AI model to generate appropriate information and send it back to the terminal.

[0657] Step 7:

[0658] After the match ends, the server collects the latest transportation information and suggests the best route home to the user. The input is a traffic information database, and the output is route information sent to the terminal. The server analyzes the necessary information, calculates an efficient route, and sends the suggested information to the terminal.

[0659] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0660] This invention is a system aimed at highly individualizing the spectator experience during sports viewing. The system incorporates an emotion engine that recognizes the spectator's emotions in real time and adjusts the information provided based on the results. The emotion engine includes algorithms for evaluating the spectator's emotional state from their facial expressions, voice tone, and feedback.

[0661] The server manages a database for collecting and analyzing information about sporting events and providing it to viewers. The server continuously monitors the progress of the match and player performance data, analyzing this data to generate commentary. The server also receives emotional data and adjusts the commentary and notification frequency according to the viewers' emotions. For example, if viewers are highly excited, the server reduces detailed commentary and focuses only on key highlights. Conversely, if viewers are bored, it provides additional information and entertainment elements.

[0662] The terminal is installed on the spectator's device and receives information transmitted from the server in real time. The emotion engine is built into the terminal and analyzes the spectator's voice and nonverbal feedback to evaluate their emotional state. Based on the emotional state, the terminal sends notifications via voice or text. If the user requests more detailed information while watching, they can ask a question using voice input, and the result is sent to the server for additional information.

[0663] This system allows users to make the viewing experience more personal. For example, if a user wants to relax during a tense moment, the emotion engine detects this and provides relaxing music or reassuring comments to the device. After the match ends, the system also takes emotional data into account and suggests the least stressful route home, improving the overall viewing experience.

[0664] In this way, the system recognizes the emotional state of the spectator and dynamically adjusts the commentary and information provided based on that state, thereby providing a personalized sports viewing experience.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] The server collects match information, player data, and event schedules from official APIs and existing databases before the start of matches and events. This ensures that the underlying data can be updated in real time.

[0668] Step 2:

[0669] The server analyzes the collected information and generates explanatory information about important events and rules during the match. This includes details about events such as goals and fouls.

[0670] Step 3:

[0671] The terminal, on the user's device, utilizes an emotion engine to begin analyzing the spectators' facial expressions and voice tone. This allows for real-time understanding of the spectators' emotional state.

[0672] Step 4:

[0673] The server receives emotion data sent from the terminal and dynamically adjusts the content of commentary and notifications based on the viewer's emotions. For example, if the viewer is highly agitated, the amount of information is reduced and the content is switched to a more relaxing one.

[0674] Step 5:

[0675] The device receives pre-arranged explanatory information sent from the server and notifies the user via voice or text. The notification method is optimized according to the user's emotional state.

[0676] Step 6:

[0677] Users can ask questions using voice input as needed, and these questions are sent from the terminal to the server. The server analyzes the questions, generates relevant information, and sends it back.

[0678] Step 7:

[0679] After the match ends, the server collects transportation information again and analyzes the optimal return route, taking into account spectator sentiment data. The terminal provides this information to the user, helping them to travel smoothly while avoiding congestion.

[0680] Step 8:

[0681] Users can choose their mode of transportation home based on the information provided by their device, allowing them to comfortably conclude their viewing experience.

[0682] (Example 2)

[0683] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0684] The spectator experience during sports events is not personalized due to the conventional, uniform information provided, making it difficult to offer information that responds to spectators' emotions and interests. Furthermore, there is insufficient information provided to ensure that spectators can return home comfortably after the event.

[0685] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0686] In this invention, the server includes means for analyzing the spectator's facial expressions, voice tone, and feedback, which are equipped with emotion recognition capabilities; means for dynamically adjusting the information provided according to the spectator's emotional state based on the analysis results, and generating commentary or entertainment elements; and means including a prompt sentence function that utilizes a generative AI model to provide detailed information based on voice input from the spectator. This enables spectators to receive personalized information according to their emotional state, improving the viewing experience and allowing them to plan a more comfortable route home.

[0687] "Emotion recognition function" is a technology that analyzes the facial expressions, voice tone, and feedback of spectators to evaluate their emotional state.

[0688] "Dynamic adjustment of information provision" is a function that allows for the instantaneous modification of commentary or entertainment elements in response to the emotional state of the viewers.

[0689] A "generative AI model" is an artificial intelligence-powered model used to provide information in response to diverse inputs, and it can generate detailed information through prompt messages.

[0690] The "prompt message function" is a technology that utilizes instructional messages to derive necessary information and responses based on voice input from spectators, using a generative AI model.

[0691] This invention is a system aimed at recognizing spectators' emotions in real time and personalizing information provision based on those emotions. This system consists of a server, terminals, and users.

[0692] The server manages a database for collecting and providing information related to sporting events to spectators. Specifically, it continuously monitors the progress of the match and player performance data, analyzes this data, and generates commentary information for spectators. Furthermore, the server receives spectator sentiment data and dynamically adjusts the content and frequency of information provided based on this data. The server incorporates a generative AI model that generates prompts that match the spectator's emotional state.

[0693] The terminal is installed on the spectator's device and receives information transmitted from the server in real time. The terminal also features emotion recognition capabilities that analyze the spectator's voice and nonverbal feedback. This allows it to assess the spectator's emotional state and provide informational notifications based on that state, either by voice or text. If the user requests further information during the event, the terminal uses voice input to send this request to the server, which then provides the additional information.

[0694] Through this system, users can enjoy a personalized viewing experience. For example, if a user only wants to see the highlights of a match, the device uses emotion recognition to detect that the user is excited and then provides commentary that focuses only on the most important information. Furthermore, when the user wants to relax, relaxation music or reassuring comments are automatically played.

[0695] As a concrete example, if a user feels nervous while watching a basketball championship game, the system will detect this nervousness using its emotion recognition function and instruct the device to provide relaxing music. A concrete example of a prompt that utilizes a generative AI model would be, "What kind of music would you recommend when the user is feeling nervous?" In this way, the system provides information that responds to the viewer's emotional changes.

[0696] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0697] Step 1:

[0698] The server collects information about sporting events from various sensors and data feeds. It takes data on the progress of matches and player performance as input and stores this information in a database. Using data analysis algorithms, it generates match highlights and important event information, which are then stored in the database. This process forms the basis for the information displayed to spectators.

[0699] Step 2:

[0700] The terminal receives data transmitted from the server in real time. The input data includes match commentary and information about spectators from the server. The emotion engine uses camera and microphone data as input to recognize the spectators' facial expressions and voice tone. This data is processed by an analysis algorithm to evaluate the spectators' emotional state in real time. As a result, information notifications optimized for each spectator become possible.

[0701] Step 3:

[0702] The server receives emotional data transmitted from the terminal and adjusts the content and frequency of existing commentary information. It uses a generative AI model that receives the viewer's emotional state, excitement level, attention level, etc., as input and generates prompt sentences based on this data. Through data processing, it dynamically creates personalized commentary and notifications tailored to the viewer's situation. This operation is performed to make the viewing experience more personal and enriching.

[0703] Step 4:

[0704] When a user inputs a question by voice through the device, the device performs speech recognition based on that audio data. The input audio data is converted into text format and sent to the server. Based on that content, the server generates appropriate answers and additional information through a generative AI model and sends them to the device. Through this process, the user can instantly obtain the information they want.

[0705] Step 5:

[0706] After the game ends, the device uses the user's emotional data and current location information to suggest a less stressful route home. It works in conjunction with traffic data, executing a route selection algorithm that considers the input transportation conditions and road congestion. The suggested route is notified to the device, helping the user to return home comfortably.

[0707] This series of processing steps allows spectators to receive information appropriate to the situation at hand, improving the overall viewing experience.

[0708] (Application Example 2)

[0709] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0710] Because the viewer's video viewing experience is generally one-way, it lacks personalized information tailored to the individual viewer's emotional state. Existing video streaming services face the challenge of providing optimal content suggestions and relevant information that reflect viewers' interests and emotions.

[0711] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0712] In this invention, the server includes a device for collecting information, means for obtaining information about events and related activities from multiple information sources, means for analyzing the acquired information and generating explanatory information that can be displayed to the viewer, and means for detecting the viewer's emotional state and adjusting the explanatory information and notification frequency based on the results. This enables personalized information provision and content optimization in accordance with the viewer's emotional state.

[0713] "Information-collecting device" refers to equipment or software used to obtain information about events and related activities related to video distribution from a variety of sources.

[0714] "Viewer" refers to individual users who watch content using video streaming services.

[0715] "Explanatory information" refers to explanations and detailed information related to the content displayed to the viewer, and is provided for the purpose of complementing the viewing experience.

[0716] "Dialogue" refers to the interaction in which a system responds to input, inquiries, and reactions from viewers.

[0717] "Transportation" refers to the means of transport or methods of transportation that viewers use to return home after the event ends.

[0718] "Emotional state" refers to the state of emotions inferred from the viewer's facial expressions, voice, and nonverbal feedback.

[0719] "Personalization" refers to adjusting services and content according to the individual characteristics and preferences of viewers, thereby optimizing the content offered.

[0720] This invention is a system that dynamically adjusts the delivery of video content based on the emotional state of the viewer. The server is equipped with a device that collects information about events and related activities from multiple sources. This information is received in real time, and a process is carried out to generate explanatory information for the viewer. In this process, natural language processing technology is used to analyze the collected data and select the information that is most relevant to the viewing experience.

[0721] The viewer's device is equipped with a camera and microphone, which are used to analyze the viewer's facial expressions and voice in real time. Software such as "Vision AI" is used for visual information analysis, and "Speech-to-Text API" is used for voice analysis. This allows the viewer's emotional state (excitement, interest, boredom, tension, etc.) to be evaluated. The device sends this emotional information to a server, which then adjusts the content based on those emotions.

[0722] For example, if a viewer sheds tears while watching an emotionally moving film, the system will suggest similar emotionally moving works and interesting information about the actors, as well as recommend other films and episodes tailored to the viewer's preferences. To achieve this, a pre-configured prompt message is sent to the server stating, "The user appears to be moved by the film they are watching. Please provide information about other emotionally moving works or interviews featuring the same actors." Based on the information in this prompt message, a generative AI model selects relevant content and provides personalized suggestions.

[0723] In this way, the system enables the personalization of content according to the viewer's emotional state, providing a more enriching viewing experience.

[0724] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0725] Step 1:

[0726] The device uses a camera and microphone to capture the viewer's facial expressions and voice in real time. The input is the viewer's video and audio data, which is analyzed by "Vision AI" and "Speech-to-Text API" to determine the viewer's emotional state. The output is metadata indicating the emotional state.

[0727] Step 2:

[0728] The terminal sends the emotional state metadata obtained in step 1 to the server. Changes in the emotional state are also taken into consideration. The input is the emotional state metadata, and the output is the transmission of metadata to the server.

[0729] Step 3:

[0730] The server collects information about related events and activities based on the received sentiment metadata. The input is sentiment metadata. It collects the necessary data from the information sources and filters the most relevant information through natural language processing. The output is the filtered candidate information.

[0731] Step 4:

[0732] The server uses a generative AI model to generate optimal content and information based on sentiment states and filtered candidate information. The input consists of candidate information and sentiment state metadata; the model then selects the most relevant content and constructs prompt sentences. The output is personalized content suggestions.

[0733] Step 5:

[0734] The server sends personalized content information to the device. The input is the content information from the server, and the device notifies the viewer via voice or text. The output is information provided to the viewer.

[0735] Step 6:

[0736] If a user expresses further interest based on the provided content and information, they will make further inquiries to the server through their device. Input is an interaction request from the viewer, which the server receives and processes / provides additional information. Output is further information or content suggestions.

[0737] Through these steps, dynamic information delivery tailored to the viewer's emotional state can be achieved, enriching the viewing experience.

[0738] 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.

[0739] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0740] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0741] 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.

[0742] Figure 9 shows an 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.

[0743] 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.

[0744] 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.

[0745] 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, motorcycles, etc., 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, for example, based 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.

[0746] 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."

[0747] 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.

[0748] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0749] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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 the like 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.

[0758] 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.

[0759] The following is further disclosed regarding the embodiments described above.

[0760] (Claim 1)

[0761] The system includes means for collecting information, and means for obtaining data on matches and related events from multiple databases,

[0762] A means for analyzing acquired data and generating explanatory information that can be displayed to spectators,

[0763] A means of sending commentary information to the spectator's device and notifying them via voice or text,

[0764] A means of receiving interaction from spectators and providing additional information as needed,

[0765] A method to collect transportation data after the match and suggest the best route home for spectators,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, comprising a voice recognition function for the spectator's terminal and analyzing the spectator's voice input.

[0769] (Claim 3)

[0770] The system according to claim 1, which optimizes notifications of local events and services based on location information provided by spectators.

[0771] "Example 1"

[0772] (Claim 1)

[0773] A means for collecting information about athletic competitions and related events from multiple information sources using an information processing device,

[0774] A means of analyzing collected information using machine learning techniques and generating explanatory materials in a format to be provided to spectators,

[0775] A means of transmitting explanatory materials to the spectators' display devices, delivering them in audio or text format,

[0776] A means of receiving two-way communication from spectators and providing additional information based on that communication,

[0777] A method to collect information on transportation options after the event and propose the most suitable return route for spectators,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, comprising a voice data processing function for a spectator's display device, and analyzing voice instructions from a spectator.

[0781] (Claim 3)

[0782] The system according to claim 1, which optimizes notifications of local events and services based on location information provided by spectators.

[0783] "Application Example 1"

[0784] (Claim 1)

[0785] A device for acquiring information, and means for collecting information on competitions and related activities from multiple information sources,

[0786] A means for analyzing collected information and generating explanatory information that can be displayed to visitors,

[0787] A means of transmitting explanatory information to the viewer's device and notifying them via audio or text,

[0788] A means of receiving dialogue from visitors and providing additional information as needed,

[0789] A method to collect transportation information after the competition and suggest the best route home for spectators,

[0790] A means of providing viewers with a deeper understanding by conveying detailed information about the match in real time via audio,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, comprising a voice recognition function for the viewer's device and analyzing the viewer's voice input.

[0794] (Claim 3)

[0795] The system according to claim 1, which optimizes notifications of local activities and services based on location information provided by the viewer.

[0796] "Example 2 of combining an emotion engine"

[0797] (Claim 1)

[0798] Equipped with emotion recognition capabilities, it provides means for analyzing the facial expressions, voice tone, and feedback of spectators,

[0799] Based on the analysis results, a means for dynamically adjusting the information provided according to the emotional state of the spectators and generating commentary or entertainment elements,

[0800] A means of sending emotional data to a server and adjusting the frequency of information provision based on the level of excitement and attention of the spectators,

[0801] A means including a prompt message function that utilizes a generative AI model to provide detailed information based on voice input from spectators,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, further comprising a function for detecting the emotional state of a spectator and providing relaxation music or comments related thereto.

[0805] (Claim 3)

[0806] The system according to claim 1, which has a function to suggest a less stressful route home, taking into account the emotional data of spectators when they return home.

[0807] "Application example 2 when combining with an emotional engine"

[0808] (Claim 1)

[0809] A device for collecting information, and means for obtaining information about events and related activities from multiple information sources,

[0810] A means for analyzing acquired information and generating explanatory information that can be displayed to viewers,

[0811] A means of transmitting explanatory information to the viewer's device and notifying them by voice or text,

[0812] A means of receiving dialogue from viewers and providing additional information as needed,

[0813] A method to collect information on transportation options after the event and suggest the best way for viewers to get home,

[0814] A means for detecting the emotional state of viewers and adjusting explanatory information and notification frequency based on the results,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, comprising a voice recognition function for the viewer's device and analyzing the viewer's voice input.

[0818] (Claim 3)

[0819] The system according to claim 1, which optimizes notifications of local activities and services based on the location information provided by the viewer. [Explanation of Symbols]

[0820] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. The system includes means for collecting information, and means for obtaining data on matches and related events from multiple databases, A means for analyzing acquired data and generating explanatory information that can be displayed to spectators, A means of sending commentary information to the spectator's device and notifying them via voice or text, A means of receiving interaction from spectators and providing additional information as needed, A method to collect transportation data after the match and suggest the best route home for spectators, A system that includes this.

2. The system according to claim 1, comprising a voice recognition function for the spectator's terminal and analyzing the spectator's voice input.

3. The system according to claim 1, which optimizes notifications of local events and services based on location information provided by spectators.

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