system

Abstract

We provide the system. [Solution] Analyze participant information collected by the imaging device, This analysis provides a means to quantify the behavior and performance of participants, A means to optimize team composition based on quantified participant behavioral information, A means of notifying participants of the optimized team composition, A system that includes this.

Description

Technical Field

[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 survival games, the first encounter rate of players is high, and it tends to become a one-sided game due to differences in individual experiences and skills. Therefore, the lack of fairness may sometimes cause new players to lose interest. Realistically, there is a lack of means to objectively grasp players' skills and form appropriate teams, so it is necessary to improve the balance of the game.

Means for Solving the Problems

[0005] This invention provides a system that collects player movements using a camera, analyzes those movements, and quantifies player actions and performance. Based on this quantified information, team composition is optimized, creating an exciting game experience while maintaining balance. Furthermore, the analyzed behavioral information is stored in a database, providing guidelines and feedback for improving players' skills. It also supports matching to strengthen cooperative relationships among players and promotes communication.

[0006] A "recording device" is a device that includes cameras and sensors used to record the movements and actions of participants in detail.

[0007] "Participant information" refers to data such as the actions, locations, and performance of players participating in the game.

[0008] "Analysis" is the process of processing collected participant information and extracting specific behavioral patterns and skill levels as numerical values.

[0009] "Quantification" refers to the process of converting participants' behavior and skills into objective, quantitative data based on analysis.

[0010] "Team composition optimization" is the process of building a fair and competitive team by considering the skill levels and interpersonal balance of the players.

[0011] "Notification" refers to the act of communicating the optimized team composition to each participant.

[0012] "Records" refer to entries in a database that continuously stores participant information analyzed during the game for later reference.

[0013] "Field matching" is a method of building smooth communication and effective cooperative relationships by taking into account the cooperativeness and play styles of participants.

[0014] "Skill improvement advice" refers to specific advice and guidelines provided based on analytical data to promote the skill development of participants. [Brief explanation of the drawing]

[0015] [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 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered 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.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered 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, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system in this invention aims to create a fair and enjoyable survival game by collecting participants' battle results in real time and providing optimal team formations based on that data.

[0037] The system uses a recording device equipped with multiple sensors and cameras to track participants' movements in detail. The recording device is mounted on a platform such as a drone above the field, recording each player's position and actions. This data is transmitted to a server in real time.

[0038] The server receives data transmitted from the camera and uses image recognition technology to analyze each player's actions. This analysis quantifies the player's behavioral characteristics and performance, and calculates their skill level. This information is stored in a database and managed as the player's profile.

[0039] The server uses the acquired skill data to propose team compositions that take into account the overall balance of participants. Specifically, it considers the skills and characteristics of each player to calculate a team composition that balances the competitiveness of each team. This team composition proposal is notified to each player via their device before the game starts.

[0040] After the game, the server provides feedback to the participants. This feedback includes specific advice to help players improve their skills. For example, "To improve your accuracy, you need to aim more quickly."

[0041] Furthermore, by utilizing accumulated data, the server conducts field matching between players and proposes approaches to deepen cooperative relationships. This allows participants to improve their skills while engaging in deeper communication.

[0042] For example, if a regular meeting has many new participants, the system will use their average performance to appropriately assign experienced players to help them improve and ensure fairness in the game. Through this entire process, players can enjoy a fair and competitive gaming experience.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] A drone uses a camera to film players on the game field. The captured video data is transmitted to a server in real time.

[0046] Step 2:

[0047] The server analyzes the received video data frame by frame. Using image recognition technology, it identifies each player and extracts their location information and movement patterns.

[0048] Step 3:

[0049] The server uses the analyzed data to quantify each player's behavior patterns. Specifically, it calculates the player's movement distance, firing frequency, and hit rate to determine their skill score.

[0050] Step 4:

[0051] The server stores each player's skill score in a database and updates each player's battle record. This allows for centralized management of each player's skill level and history.

[0052] Step 5:

[0053] The server uses accumulated skill data to run an algorithm that proposes balanced team compositions. Teams are formed to minimize skill differences between players.

[0054] Step 6:

[0055] The device receives optimized team information sent from the server and notifies each player. The notification includes information about the team the player belongs to and an overview of the game rules.

[0056] Step 7:

[0057] Users participate in the game and, after it ends, receive feedback generated by the server. This feedback includes specific advice for improving their skills.

[0058] Step 8:

[0059] The server utilizes accumulated player data to suggest field matching that maximizes cooperation between players. This facilitates smoother communication and improves skills.

[0060] (Example 1)

[0061] 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."

[0062] In current survival games, differences in participants' skill levels can compromise the fairness of the game. Therefore, there is a need for an automated, real-time system that creates balanced teams that participants can enjoy. Furthermore, there is insufficient mechanism for facilitating post-game feedback and building relationships among participants.

[0063] 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.

[0064] In this invention, the server includes means for receiving data from a measuring device to capture participants' behavior, means for analyzing the received data and quantifying participants' behavioral characteristics and performance, means for optimizing team formation considering the overall balance of participants based on the quantified behavioral information, and means for notifying participants of the optimized team formation via an information terminal. This improves the fairness of the game and enables an optimal game experience tailored to each participant's skill level. Furthermore, the feedback provided after the game allows participants to obtain specific guidance for improving their skills.

[0065] "Participant" refers to an individual player who uses the system to participate in a survival game.

[0066] A "measuring device" refers to a device equipped with multiple sensors and cameras used to track the location and movements of participants in real time.

[0067] "Means of receiving data" refers to communication protocols and hardware used to transfer information from measuring devices to a server for analysis.

[0068] "Behavioral characteristics" refer to detailed behavioral patterns of participants during a game, such as their actions, reactions, and strategic decisions.

[0069] "Methods for quantifying performance" refer to algorithms and software that express participants' behavioral characteristics as quantitative data and calculate their skill levels within the game.

[0070] "Methods for optimizing team composition" refer to algorithms or programs that generate the optimal team composition to maintain fairness in the game, based on the skills and characteristics of the participants.

[0071] "Information terminal" refers to a device or interface used by participants to form teams and receive feedback before the game starts.

[0072] "Feedback" refers to specific advice and guidelines provided after a game to help participants improve their skills and performance.

[0073] The system in this invention provides advanced technology to ensure participant skill and game fairness in survival games. A server plays a central role, analyzing participant data obtained from measuring devices and forming appropriate teams.

[0074] The server uses a measurement device equipped with multiple sensors and cameras mounted on a drone to track the location and movements of participants in the field in detail. This measurement device collects real-time data from participants and transmits it to the server via an internet connection. The server uses image recognition libraries such as TENSORFLOW® and OpenCV to analyze this data and quantify the participants' behavioral characteristics and performance.

[0075] Based on quantified data, the server uses a generative AI model to create balanced team compositions. Specifically, it calculates team compositions that balance the competitiveness of each participant, taking into account their individual skills. These team compositions are then communicated to participants via their devices before the game begins.

[0076] After the game ends, the server generates feedback based on the analysis results to help participants improve their skills. This feedback includes specific analysis results and recommended training methods to enhance the players' abilities.

[0077] Furthermore, the server proposes a field matching strategy to promote engagement and relationship building among participants. It utilizes participants' past performance data to optimize member placement for future game events. This process deepens cooperation among participants and improves the overall game experience.

[0078] An example of a prompt message might be, "In a regular game event with many new participants, please analyze each player's skill data and propose team compositions that appropriately place experienced players." This system allows participants to enjoy a fair and enjoyable gaming experience based on well-analyzed data.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server receives real-time data from the measuring devices. The input consists of participant location and movement data acquired by multiple sensors and cameras. This data is retrieved via the network and converted into a format that can be processed within the system. Specifically, the data is passed to the analysis module in JSON format.

[0082] Step 2:

[0083] The server analyzes the received data using an image recognition library. The input consists of location data and motion data obtained in step 1. The server uses this data to quantify the participants' behavioral characteristics and performance. Specifically, it uses TensorFlow to recognize movements and gestures and calculates the players' skill items (such as hit rate and evasion ability). This process generates quantified skill data as output.

[0084] Step 3:

[0085] The server uses a generative AI model to optimize team composition based on quantified behavioral data. The input is the skill data for each participant obtained in step 2. The server uses the AI ​​model to automatically generate team compositions that ensure equal competitiveness, taking into account the characteristics of each player. The output of this process is the optimized team composition proposal.

[0086] Step 4:

[0087] The server notifies participants of the generated team composition via their terminals. The input is the proposed team composition created in step 3. This composition information is sent to the user's terminal and displayed as visually verifiable information. Specifically, a notification message is displayed on the terminal's screen, informing participants of the team information. This output is the team composition result notified to each player.

[0088] Step 5:

[0089] After the game ends, the server provides feedback to the participants. The input consists of behavioral characteristics and player performance data from Step 2. The server generates feedback from this data to help improve skills and sends it to the participants. Specific examples include advice such as, "To improve your accuracy, you should practice reflexes." The output is an individual feedback message.

[0090] Step 6:

[0091] The server proposes field matching for the next event based on game history data. The input is data showing past game performance and cooperative relationships. The server combines this information to generate new matching proposals. Specifically, it analyzes past performance data and proposes new team formations for players. This output is the field matching proposal to be used in the next game event.

[0092] (Application Example 1)

[0093] 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."

[0094] In traditional brick-and-mortar stores, it has been difficult to provide services based on the diverse purchasing trends of customers. This has resulted in the inability to offer personalized shopping experiences for each customer, hindering improvements in customer satisfaction.

[0095] 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.

[0096] In this invention, the server includes means for analyzing participant information collected by a camera and optimizing purchasing trends based on the analysis; means for notifying customers of the optimized purchasing trends; and means for storing the analyzed participant behavior information as a record and providing advice to improve the customer's purchasing experience. This makes it possible to provide personalized information to each customer in physical stores and to increase customer satisfaction.

[0097] A "recording device" is a device used to record participants' actions and performance, and is equipped with sensors and cameras.

[0098] "Participant information" refers to data collected by the camera, including information such as participants' behavior and purchasing tendencies.

[0099] "Analysis" refers to the process of analyzing data based on collected participant information and quantifying participants' behavior and performance.

[0100] "Quantification" is the process of converting participants' behavior and performance into quantitative data.

[0101] "Methods for optimizing purchasing trends" refer to the process of generating information to provide individual customers with the most optimal purchasing experience, based on quantified participant behavioral data.

[0102] "Means of notifying customers" refers to methods of delivering information to customers based on optimized purchasing trends, including smartphone applications and other communication methods.

[0103] "Storing as a record" refers to the act of saving the analyzed participant behavior information in a database or similar system for future use.

[0104] "Means of providing advice" refers to methods of providing customers with personalized purchasing advice and information based on stored data.

[0105] In this invention, the server constructs a system for collecting and analyzing user purchasing behavior. The hardware used includes sensors and cameras within the store, as well as the user's smartphone. The sensors and cameras record the movement and purchasing behavior of participants (customers) in real time, and the smartphone supplements this data using GPS and Bluetooth functions.

[0106] The collected data is sent to a server built within the cloud service. On the server, this data is analyzed using an AI model to quantify and optimize customer purchasing trends. Machine learning algorithms are applied to the analysis to generate personalized product recommendations and campaign information for each customer.

[0107] The generated suggestions and information are notified through an application installed on the customer's smartphone. These notifications include special offers, promotions, and product recommendations tailored to the customer's interests. This enhances the user's in-store shopping experience and increases their satisfaction.

[0108] For example, if a customer is interested in a vegan diet and frequently purchases vegan-related products based on their past purchase history, they will receive information about new products and promotions tailored to that trend. The system also uses prompts like the following to input data into an AI model and generate effective suggestions: "Based on past purchase data, please suggest the best products for this customer. Since they have purchased vegan products in the past, they are likely interested in vegan-related products. Please consider recently added new products."

[0109] This format enables effective customer engagement and personalized services in physical stores, leading to improved operational efficiency and user experience.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server collects customer behavior data using sensors and cameras within the store. This data includes customer movement patterns, dwell time, and interest in specific products. Input is raw data from sensors and cameras, and output is stored in a database as individual customer behavior information.

[0113] Step 2:

[0114] The terminal (smartphone) uses Bluetooth and GPS functions to acquire customer location information. It receives real-time location data sent from the smartphone as input and sends information to the server that visualizes the customer's movements and current location within the store as output.

[0115] Step 3:

[0116] The server uses an AI model to analyze customer purchasing trends based on collected behavioral data. The input is stored customer behavior data, and the output generates an optimal product list and purchase recommendations for each customer. The AI ​​model receives data based on prompt messages, generating accurate product suggestions.

[0117] Step 4:

[0118] The server notifies the customer's device of the generated purchase suggestions. Using the generated product list and suggestion content as input, the output is a notification message displayed on the customer's smartphone. This allows customers to receive information based on their interests in real time.

[0119] Step 5:

[0120] Users (customers) receive information notified on their smartphones and make purchasing decisions based on that information. The input is the information displayed on the smartphone, and the output is a change in their purchasing behavior within the store. This leads to improved customer satisfaction.

[0121] 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.

[0122] This invention provides a system that recognizes participants' emotions and uses that information to improve the quality of the game experience. The system consists of a camera, an emotion engine, an analysis server, and a user terminal.

[0123] The camera monitors the game field and participants, collecting visual data in real time. This allows for the acquisition of basic information about players' movements and actions.

[0124] The emotion engine analyzes acquired visual data and audio input to infer emotions from participants' facial expressions and tone of voice. This emotion data indicates the player's psychological state during the game.

[0125] The server comprehensively analyzes emotional data transmitted from the emotion engine and behavioral data obtained from the camera. This information is combined with each player's skill evaluation and performance during the game to generate a more detailed skill score.

[0126] Furthermore, the server uses the collected data to create teams that take into account the players' psychological state. For example, it might pair a nervous player with an experienced collaborator to help them relax. This kind of team composition, which considers psychological factors, aims to improve players' performance in the game.

[0127] The device not only sends players notifications of team composition based on analysis results, but also provides post-game feedback, including advice for skill improvement based on the player's emotional changes. For example, it may include specific advice such as, "Positive feedback can boost self-efficacy and lead to improved performance."

[0128] Furthermore, the server utilizes emotional data to suggest field matches that are expected to facilitate smooth communication. This process makes it easier for participants to build trust with each other, resulting in a cooperative and enriching gaming experience.

[0129] For example, if player A becomes frustrated during a game, the emotion engine detects the change in their facial expression and sends emotional data. The server analyzes this data and, in the next team formation, suggests teaming player A with player B, who is good at entertaining others, in order to alleviate their mood. In this way, the use of the emotion engine dramatically improves participant satisfaction in the game experience.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The recording equipment constantly monitors the entire game field, recording participants' movements and facial expressions in real time. This allows for the acquisition of visual and audio data.

[0133] Step 2:

[0134] The emotion engine analyzes visual and auditory data to infer the emotional state of participants based on their facial expressions and tone of voice. This analysis result is sent to the server as emotion data.

[0135] Step 3:

[0136] The server simultaneously processes emotional data and behavioral data transmitted from the recording device, and calculates a skill score that integrates emotional information into each player's skill evaluation. This creates a comprehensive player profile that takes psychological factors into account.

[0137] Step 4:

[0138] The server optimizes team composition based on the acquired skill scores, tailoring it to each player's psychological state. Teams are formed to reflect players' levels of tension and friendliness, and adjusted to achieve the most positive results.

[0139] Step 5:

[0140] The terminal receives optimized team information calculated by the server and notifies each player. The notification includes the changed team members and their roles.

[0141] Step 6:

[0142] Users review the notified information and start the game as their assigned team. Team formation based on sentiment data promotes smooth communication and cooperative gameplay.

[0143] Step 7:

[0144] After the game ends, the server provides feedback to the participants. This feedback includes specific advice for skill improvement based on emotional changes and performance results. Users can use this to prepare for their next game.

[0145] Step 8:

[0146] The server uses collected emotional data to analyze how well certain players are compatible with each other and makes suggestions for future field matching. This allows for continuous improvement that takes emotional compatibility into account.

[0147] (Example 2)

[0148] 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".

[0149] Traditional game systems collect and analyze data without considering participants' emotions or psychological states, resulting in team formation and skill improvement advice that did not adequately contribute to participant satisfaction. Therefore, there is a need for optimized team formation that reflects the diverse psychological states of participants, as well as feedback and skill improvement advice based on emotional changes.

[0150] 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.

[0151] In this invention, the server includes means for analyzing participant information collected by a camera, means for integrating quantified participant behavioral and emotional information to generate a skill score that takes into account the player's psychological state, and means for notifying participants of the optimized team composition and providing advice for skill improvement based on emotional changes. This makes it possible to optimize team composition based on the psychological state of each participant and to provide appropriate feedback and advice for skill improvement that utilizes the emotional changes of the participants.

[0152] A "camera equipment" is a device used to monitor the game field and participants in real time and to collect visual and audio data.

[0153] "Participant information" refers to data such as participants' behavior, facial expressions, and voice collected by the recording device.

[0154] "Analysis" is the act of interpreting the meaning of data using a specific algorithm based on participant information and quantifying it.

[0155] "Quantification" is the process of expressing data obtained through analysis as objective numerical values.

[0156] "Behavioral information" refers to data about participants' actions, such as their movements and play style.

[0157] "Emotional information" refers to data on the emotional state inferred from the participants' facial expressions and tone of voice.

[0158] A "skill score" is a numerical value that indicates an individual's ability, calculated from the combined results of participants' behavioral and emotional information.

[0159] "Team formation" is the process of combining multiple participants to create a team.

[0160] "Feedback" refers to advice and comments provided to improve performance, based on the emotional changes of participants.

[0161] "Skill improvement advice" refers to specific advice and guidance provided to enhance participants' abilities.

[0162] "Psychological state" refers to the mental state or mental tendencies interpreted from the emotional information of the participants.

[0163] This invention is a system that recognizes participants' emotions and uses that information to improve the quality of the game experience. The system consists of a camera, an emotion engine, an analysis server, and a user terminal.

[0164] The user collects real-time visual data of the game field and participants using a recording device. This device incorporates a high-precision camera and microphone to record players' movements, facial expressions, and voice tones in detail.

[0165] The server transmits data acquired from the camera to the emotion engine. The emotion engine uses machine learning algorithms to analyze the visual and audio data to infer the participants' emotional information. This emotional information includes the various emotional states that the participants experience during the game.

[0166] Through collaboration between the emotion engine and the server, the server integrates participant behavioral and emotional information. This generates a skill score that takes into account the player's psychological state. This skill score is used to optimize team composition.

[0167] The terminal notifies participants of the optimized team composition sent from the server. After the game, it also provides feedback with advice for skill improvement based on emotional changes. This allows participants to receive specific advice that helps them improve their play style.

[0168] For example, if player A shows an expression of frustration during a game, the emotion engine analyzes this information and sends it to the server. The server combines this information with behavioral data to suggest a team composition that will help player A relax. Furthermore, the device improves the quality of the game experience by providing advice to player A, such as "Take a deep breath."

[0169] An example of a prompt message might be a request to "design a system that analyzes the emotions of game participants in real time and provides the optimal team composition based on that analysis."

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user acquires visual and audio data of the game field and participants in real time through a recording device. Inputs include participants' movements, facial expressions, and voice tone captured by a high-precision camera and microphone. This results in output of participant behavioral information and audio information.

[0173] Step 2:

[0174] The server inputs visual and audio data transmitted from the camera into the emotion engine. The emotion engine analyzes this data using a generative AI model to infer the participant's emotional information. Based on the input data, the emotion engine performs facial recognition algorithms and audio analysis, generating data indicating the participant's emotional state as output.

[0175] Step 3:

[0176] The server integrates emotional information obtained from the emotion engine with behavioral information from the camera. It uses emotional and behavioral data as input and performs data analysis to integrate them through recording in a database. As a result, a skill score that takes the player's psychological state into account is output.

[0177] Step 4:

[0178] The server creates optimal team compositions based on the generated skill scores. The input is each participant's skill score, and an optimization algorithm is used to create teams that balance participants' mental state and abilities. The output is an optimized team composition for each player.

[0179] Step 5:

[0180] The terminal notifies participants of optimized team formations created by the server and advice for skill improvement. The input is team formation information and feedback advice from the server, and the output is a notification message sent to the participants. For example, the terminal might display the advice, "You should take some deep breaths to relax."

[0181] Step 6:

[0182] The server uses participant emotional data to suggest cooperative field matching. It uses participant emotional and skill data as input and analyzes compatibility using a generative AI model. This process produces recommended matching to facilitate trust building among participants.

[0183] (Application Example 2)

[0184] 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".

[0185] Current online content distribution services struggle to provide interactive experiences that reflect viewers' emotions and psychological states in real time, thus failing to maximize viewer satisfaction and immersion in the content.

[0186] 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.

[0187] In this invention, the server includes means for analyzing participant information and quantifying participants' behavior and performance, means for recognizing participants' emotional states in real time and adjusting interactive content, and means for providing personalized content suggestions to each viewer based on emotional data. This makes it possible to provide each viewer with an optimal content experience based on their emotions.

[0188] A "recording device" is a device used to collect participants' visual and audio data in real time and to monitor their emotions and behavior.

[0189] "Analysis" is the process of quantifying and judging participants' behavior and emotional states based on collected visual and auditory data.

[0190] "Quantification" is the process of expressing information such as participants' behavior, performance, and emotional state as quantitative data.

[0191] "Methods for optimizing team composition" refer to methods that use participants' behavioral data and emotional information to determine the most efficient and collaborative team structure.

[0192] "Means of notification" refers to communication methods used to inform participants of the results of optimized team formations and content adjustments.

[0193] "Emotional state" refers to the psychological and emotional responses exhibited by participants, which can be inferred from changes in facial expressions and voice.

[0194] "Interactive content" is content that changes in response to participants' reactions and emotions, and is an element that dynamically personalizes the viewing experience.

[0195] "Personalized content suggestions" is a feature that recommends content deemed most suitable for each participant based on their emotional data.

[0196] To implement this invention, the following system configuration is introduced.

[0197] First, the server receives visual and audio data from the recording device in order to process the data collected from numerous participants. The recording device uses devices such as smart glasses to collect participants' facial expressions and voice tone in real time. This information is analyzed through an emotion engine, and the participants' emotional state is quantified. The emotion engine uses machine learning models, and software such as OpenCV and TensorFlow are used here.

[0198] Next, the server integrates the analyzed emotional and behavioral data to adjust content according to the psychological state of each participant. The server runs on a cloud service (e.g., AWS® Lambda) and efficiently processes and analyzes viewer data. Based on the viewer's emotional data, it interactively provides personalized content suggestions. This process uses prompts generated by an AI model, which may include instructions such as, "Analyze the viewer's facial expressions while they are watching the movie and suggest personalized content appropriate to the situation. If the viewer's emotions change, suggest what content would be appropriate."

[0199] Finally, the device receives instructions from the server and notifies participants of this feedback and adjusted content. The device uses common devices such as smartphones and tablets, providing an environment where participants can easily receive information. For example, if a participant feels tense while watching a suspense movie, the system will suggest a comical scene to adjust the viewing experience for the participant to be more comfortable.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The user wears smart glasses, and visual and audio data are collected by a recording device. The input is real-time facial expressions and voice, and the output is data sent to an emotion engine. At this stage, concrete actions are taken to accurately capture the user's facial movements and voice tone.

[0203] Step 2:

[0204] The server analyzes the visual and auditory data received via the emotion engine to determine the participant's emotional state. The input is the output data from step 1, and the output is numerical data of the analyzed emotion. Specifically, a generative AI model is used, and emotional characteristics are quantified using OpenCV and TensorFlow.

[0205] Step 3:

[0206] The server integrates quantified sentiment data with other behavioral data to determine the optimal content adjustment or team composition for each individual user. Inputs are numerical sentiment data and behavioral history, while outputs are team composition and content suggestions. Specifically, data analysis and information integration are performed on a cloud service to determine specific recommended actions.

[0207] Step 4:

[0208] The device notifies the user of suggestions and feedback sent from the server. The input is the suggestion data obtained in step 3, and the output is the notifications and messages the user receives. Specifically, it provides an environment where participants can immediately check the information by displaying notifications on the screen of their smartphone or tablet.

[0209] Step 5:

[0210] Based on notifications from their device, users select content that matches their emotions and continue their viewing experience. The input is the notification content from the device, and the output is the content selected by the user. For example, a specific action could be the user tapping the screen to switch to comical content suggested during a tense scene.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] The system in this invention aims to create a fair and enjoyable survival game by collecting participants' battle results in real time and providing optimal team formations based on that data.

[0228] The system uses a recording device equipped with multiple sensors and cameras to track participants' movements in detail. The recording device is mounted on a platform such as a drone above the field, recording each player's position and actions. This data is transmitted to a server in real time.

[0229] The server receives data transmitted from the camera and uses image recognition technology to analyze each player's actions. This analysis quantifies the player's behavioral characteristics and performance, and calculates their skill level. This information is stored in a database and managed as the player's profile.

[0230] The server uses the acquired skill data to propose team compositions that take into account the overall balance of participants. Specifically, it considers the skills and characteristics of each player to calculate a team composition that balances the competitiveness of each team. This team composition proposal is notified to each player via their device before the game starts.

[0231] After the game, the server provides feedback to the participants. This feedback includes specific advice to help players improve their skills. For example, "To improve your accuracy, you need to aim more quickly."

[0232] Furthermore, by utilizing accumulated data, the server conducts field matching between players and proposes approaches to deepen cooperative relationships. This allows participants to improve their skills while engaging in deeper communication.

[0233] For example, if a regular meeting has many new participants, the system will use their average performance to appropriately assign experienced players to help them improve and ensure fairness in the game. Through this entire process, players can enjoy a fair and competitive gaming experience.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] A drone uses a camera to film players on the game field. The captured video data is transmitted to a server in real time.

[0237] Step 2:

[0238] The server analyzes the received video data frame by frame. Using image recognition technology, it identifies each player and extracts their location information and movement patterns.

[0239] Step 3:

[0240] The server uses the analyzed data to quantify each player's behavior patterns. Specifically, it calculates the player's movement distance, firing frequency, and hit rate to determine their skill score.

[0241] Step 4:

[0242] The server stores each player's skill score in a database and updates each player's battle record. This allows for centralized management of each player's skill level and history.

[0243] Step 5:

[0244] The server uses accumulated skill data to run an algorithm that proposes balanced team compositions. Teams are formed to minimize skill differences between players.

[0245] Step 6:

[0246] The device receives optimized team information sent from the server and notifies each player. The notification includes information about the team the player belongs to and an overview of the game rules.

[0247] Step 7:

[0248] Users participate in the game and, after it ends, receive feedback generated by the server. This feedback includes specific advice for improving their skills.

[0249] Step 8:

[0250] The server utilizes accumulated player data to suggest field matching that maximizes cooperation between players. This facilitates smoother communication and improves skills.

[0251] (Example 1)

[0252] 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."

[0253] In current survival games, differences in participants' skill levels can compromise the fairness of the game. Therefore, there is a need for an automated, real-time system that creates balanced teams that participants can enjoy. Furthermore, there is insufficient mechanism for facilitating post-game feedback and building relationships among participants.

[0254] 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.

[0255] In this invention, the server includes means for receiving data from a measuring device to capture participants' behavior, means for analyzing the received data and quantifying participants' behavioral characteristics and performance, means for optimizing team formation considering the overall balance of participants based on the quantified behavioral information, and means for notifying participants of the optimized team formation via an information terminal. This improves the fairness of the game and enables an optimal game experience tailored to each participant's skill level. Furthermore, the feedback provided after the game allows participants to obtain specific guidance for improving their skills.

[0256] "Participant" refers to an individual player who uses the system to participate in a survival game.

[0257] A "measuring device" refers to a device equipped with multiple sensors and cameras used to track the location and movements of participants in real time.

[0258] "Means of receiving data" refers to communication protocols and hardware used to transfer information from measuring devices to a server for analysis.

[0259] "Behavioral characteristics" refer to detailed behavioral patterns of participants during a game, such as their actions, reactions, and strategic decisions.

[0260] "Methods for quantifying performance" refer to algorithms and software that express participants' behavioral characteristics as quantitative data and calculate their skill levels within the game.

[0261] "Methods for optimizing team composition" refer to algorithms or programs that generate the optimal team composition to maintain fairness in the game, based on the skills and characteristics of the participants.

[0262] "Information terminal" refers to a device or interface used by participants to form teams and receive feedback before the game starts.

[0263] "Feedback" refers to specific advice and guidelines provided after a game to help participants improve their skills and performance.

[0264] The system in this invention provides advanced technology to ensure participant skill and game fairness in survival games. A server plays a central role, analyzing participant data obtained from measuring devices and forming appropriate teams.

[0265] The server uses a measurement device equipped with multiple sensors and cameras mounted on a drone to track the location and movements of participants in the field in detail. This measurement device collects real-time data from participants and transmits it to the server via an internet connection. The server uses image recognition libraries such as TensorFlow and OpenCV to analyze this data and quantify the participants' behavioral characteristics and performance.

[0266] Based on quantified data, the server uses a generative AI model to create balanced team compositions. Specifically, it calculates team compositions that balance the competitiveness of each participant, taking into account their individual skills. These team compositions are then communicated to participants via their devices before the game begins.

[0267] After the game ends, the server generates feedback based on the analysis results to help participants improve their skills. This feedback includes specific analysis results and recommended training methods to enhance the players' abilities.

[0268] Furthermore, the server proposes a field matching strategy to promote engagement and relationship building among participants. It utilizes participants' past performance data to optimize member placement for future game events. This process deepens cooperation among participants and improves the overall game experience.

[0269] An example of a prompt message might be, "In a regular game event with many new participants, please analyze each player's skill data and propose team compositions that appropriately place experienced players." This system allows participants to enjoy a fair and enjoyable gaming experience based on well-analyzed data.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The server receives real-time data from the measuring devices. The input consists of participant location and movement data acquired by multiple sensors and cameras. This data is retrieved via the network and converted into a format that can be processed within the system. Specifically, the data is passed to the analysis module in JSON format.

[0273] Step 2:

[0274] The server analyzes the received data using an image recognition library. The input consists of location data and motion data obtained in step 1. The server uses this data to quantify the participants' behavioral characteristics and performance. Specifically, it uses TensorFlow to recognize movements and gestures and calculates the players' skill items (such as hit rate and evasion ability). This process generates quantified skill data as output.

[0275] Step 3:

[0276] The server uses a generative AI model to optimize team composition based on quantified behavioral data. The input is the skill data for each participant obtained in step 2. The server uses the AI ​​model to automatically generate team compositions that ensure equal competitiveness, taking into account the characteristics of each player. The output of this process is the optimized team composition proposal.

[0277] Step 4:

[0278] The server notifies participants of the generated team composition via their terminals. The input is the proposed team composition created in step 3. This composition information is sent to the user's terminal and displayed as visually verifiable information. Specifically, a notification message is displayed on the terminal's screen, informing participants of the team information. This output is the team composition result notified to each player.

[0279] Step 5:

[0280] After the game ends, the server provides feedback to the participants. The input consists of behavioral characteristics and player performance data from Step 2. The server generates feedback from this data to help improve skills and sends it to the participants. Specific examples include advice such as, "To improve your accuracy, you should practice reflexes." The output is an individual feedback message.

[0281] Step 6:

[0282] Based on the game history data, the server proposes field matching for the next time. The input is data indicating past game performance and cooperation relationships. The server combines this information to generate a new matching plan. As a specific operation, it analyzes past performance data and proposes a new team formation for players. This output is the field matching plan to be used in the next game event.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In a conventional physical store, it has been difficult to provide services based on diverse purchasing tendencies of customers. For this reason, there is a problem that a personalized shopping experience cannot be provided for each customer, and customer satisfaction cannot be improved.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0287] In this invention, the server includes means for analyzing participant information collected by a photographing device and optimizing purchasing tendencies by the analysis, means for notifying customers of the optimized purchasing tendencies, and means for storing the analyzed action information of the participants as records and providing advice for improving the customer's purchasing experience. Thereby, it becomes possible to provide personalized information for each customer in a physical store, and it becomes possible to enhance customer satisfaction.

[0288] The "photographing device" is a device used to record the actions and achievements of participants, and is equipped with sensors and cameras.

[0289] "Participant information" refers to data collected by the camera, including information such as participants' behavior and purchasing tendencies.

[0290] "Analysis" refers to the process of analyzing data based on collected participant information and quantifying participants' behavior and performance.

[0291] "Quantification" is the process of converting participants' behavior and performance into quantitative data.

[0292] "Methods for optimizing purchasing trends" refer to the process of generating information to provide individual customers with the most optimal purchasing experience, based on quantified participant behavioral data.

[0293] "Means of notifying customers" refers to methods of delivering information to customers based on optimized purchasing trends, including smartphone applications and other communication methods.

[0294] "Storing as a record" refers to the act of saving the analyzed participant behavior information in a database or similar system for future use.

[0295] "Means of providing advice" refers to methods of providing customers with personalized purchasing advice and information based on stored data.

[0296] In this invention, the server constructs a system for collecting and analyzing user purchasing behavior. The hardware used includes sensors and cameras within the store, as well as the user's smartphone. The sensors and cameras record the movement and purchasing behavior of participants (customers) in real time, and the smartphone supplements this data using GPS and Bluetooth functions.

[0297] The collected data is sent to a server built within the cloud service. On the server, this data is analyzed using an AI model to quantify and optimize customer purchasing trends. Machine learning algorithms are applied to the analysis to generate personalized product recommendations and campaign information for each customer.

[0298] The generated suggestions and information are notified through an application installed on the customer's smartphone. These notifications include special offers, promotions, and product recommendations tailored to the customer's interests. This enhances the user's in-store shopping experience and increases their satisfaction.

[0299] For example, if a customer is interested in a vegan diet and frequently purchases vegan-related products based on their past purchase history, they will receive information about new products and promotions tailored to that trend. The system also uses prompts like the following to input data into an AI model and generate effective suggestions: "Based on past purchase data, please suggest the best products for this customer. Since they have purchased vegan products in the past, they are likely interested in vegan-related products. Please consider recently added new products."

[0300] This format enables effective customer engagement and personalized services in physical stores, leading to improved operational efficiency and user experience.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] The server collects customer behavior data using sensors and cameras within the store. This data includes customer movement patterns, dwell time, and interest in specific products. Input is raw data from sensors and cameras, and output is stored in a database as individual customer behavior information.

[0304] Step 2:

[0305] The terminal (smartphone) utilizes Bluetooth and GPS functions to obtain the customer's location information. It receives real-time location data sent from the smartphone as input, and transmits information that visualizes the customer's movement and current location within the store to the server as output.

[0306] Step 3:

[0307] The server analyzes the customer's purchasing tendency using an AI model based on the collected behavior data. The input is the stored customer behavior data, and as output, an optimal product list and purchase recommendations are generated for each customer. By inputting data into the AI model based on the prompt text, accurate product proposals are generated.

[0308] Step 4:

[0309] The server notifies the customer's terminal of the generated purchase proposals. Using the generated product list and proposal content as input, the output is a notification message displayed on the customer's smartphone. Through this operation, the customer can receive information based on their own interests in real time.

[0310] Step 5:

[0311] The user (customer) receives the information notified on the smartphone and conducts a purchase action based on it. The input is the information displayed on the smartphone, and as output, a change can be seen in the purchase action of the products within the store. As a result, customer satisfaction is improved.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0313] This invention provides a system that recognizes participants' emotions and uses that information to improve the quality of the game experience. The system consists of a camera, an emotion engine, an analysis server, and a user terminal.

[0314] The camera monitors the game field and participants, collecting visual data in real time. This allows for the acquisition of basic information about players' movements and actions.

[0315] The emotion engine analyzes acquired visual data and audio input to infer emotions from participants' facial expressions and tone of voice. This emotion data indicates the player's psychological state during the game.

[0316] The server comprehensively analyzes emotional data transmitted from the emotion engine and behavioral data obtained from the camera. This information is combined with each player's skill evaluation and performance during the game to generate a more detailed skill score.

[0317] Furthermore, the server uses the collected data to create teams that take into account the players' psychological state. For example, it might pair a nervous player with an experienced collaborator to help them relax. This kind of team composition, which considers psychological factors, aims to improve players' performance in the game.

[0318] The device not only sends players notifications of team composition based on analysis results, but also provides post-game feedback, including advice for skill improvement based on the player's emotional changes. For example, it may include specific advice such as, "Positive feedback can boost self-efficacy and lead to improved performance."

[0319] Furthermore, the server utilizes emotional data to suggest field matches that are expected to facilitate smooth communication. This process makes it easier for participants to build trust with each other, resulting in a cooperative and enriching gaming experience.

[0320] For example, if player A becomes frustrated during a game, the emotion engine detects the change in their facial expression and sends emotional data. The server analyzes this data and, in the next team formation, suggests teaming player A with player B, who is good at entertaining others, in order to alleviate their mood. In this way, the use of the emotion engine dramatically improves participant satisfaction in the game experience.

[0321] The following describes the processing flow.

[0322] Step 1:

[0323] The recording equipment constantly monitors the entire game field, recording participants' movements and facial expressions in real time. This allows for the acquisition of visual and audio data.

[0324] Step 2:

[0325] The emotion engine analyzes visual and auditory data to infer the emotional state of participants based on their facial expressions and tone of voice. This analysis result is sent to the server as emotion data.

[0326] Step 3:

[0327] The server simultaneously processes emotional data and behavioral data transmitted from the recording device, and calculates a skill score that integrates emotional information into each player's skill evaluation. This creates a comprehensive player profile that takes psychological factors into account.

[0328] Step 4:

[0329] The server optimizes team composition based on the acquired skill scores, tailoring it to each player's psychological state. Teams are formed to reflect players' levels of tension and friendliness, and adjusted to achieve the most positive results.

[0330] Step 5:

[0331] The terminal receives optimized team information calculated by the server and notifies each player. The notification includes the changed team members and their roles.

[0332] Step 6:

[0333] Users review the notified information and start the game as their assigned team. Team formation based on sentiment data promotes smooth communication and cooperative gameplay.

[0334] Step 7:

[0335] After the game ends, the server provides feedback to the participants. This feedback includes specific advice for skill improvement based on emotional changes and performance results. Users can use this to prepare for their next game.

[0336] Step 8:

[0337] The server uses collected emotional data to analyze how well certain players are compatible with each other and makes suggestions for future field matching. This allows for continuous improvement that takes emotional compatibility into account.

[0338] (Example 2)

[0339] 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".

[0340] Traditional game systems collect and analyze data without considering participants' emotions or psychological states, resulting in team formation and skill improvement advice that did not adequately contribute to participant satisfaction. Therefore, there is a need for optimized team formation that reflects the diverse psychological states of participants, as well as feedback and skill improvement advice based on emotional changes.

[0341] 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.

[0342] In this invention, the server includes means for analyzing participant information collected by a camera, means for integrating quantified participant behavioral and emotional information to generate a skill score that takes into account the player's psychological state, and means for notifying participants of the optimized team composition and providing advice for skill improvement based on emotional changes. This makes it possible to optimize team composition based on the psychological state of each participant and to provide appropriate feedback and advice for skill improvement that utilizes the emotional changes of the participants.

[0343] A "camera equipment" is a device used to monitor the game field and participants in real time and to collect visual and audio data.

[0344] "Participant information" refers to data such as participants' behavior, facial expressions, and voice collected by the recording device.

[0345] "Analysis" is the act of interpreting the meaning of data using a specific algorithm based on participant information and quantifying it.

[0346] "Quantification" is the process of expressing data obtained through analysis as objective numerical values.

[0347] "Behavioral information" refers to data about participants' actions, such as their movements and play style.

[0348] "Emotional information" refers to data on the emotional state inferred from the participants' facial expressions and tone of voice.

[0349] A "skill score" is a numerical value that indicates an individual's ability, calculated from the combined results of participants' behavioral and emotional information.

[0350] "Team formation" is the process of combining multiple participants to create a team.

[0351] "Feedback" refers to advice and comments provided to improve performance, based on the emotional changes of participants.

[0352] "Skill improvement advice" refers to specific advice and guidance provided to enhance participants' abilities.

[0353] "Psychological state" refers to the mental state or mental tendencies interpreted from the emotional information of the participants.

[0354] This invention is a system that recognizes participants' emotions and uses that information to improve the quality of the game experience. The system consists of a camera, an emotion engine, an analysis server, and a user terminal.

[0355] The user collects real-time visual data of the game field and participants using a recording device. This device incorporates a high-precision camera and microphone to record players' movements, facial expressions, and voice tones in detail.

[0356] The server transmits data acquired from the camera to the emotion engine. The emotion engine uses machine learning algorithms to analyze the visual and audio data to infer the participants' emotional information. This emotional information includes the various emotional states that the participants experience during the game.

[0357] Through collaboration between the emotion engine and the server, the server integrates participant behavioral and emotional information. This generates a skill score that takes into account the player's psychological state. This skill score is used to optimize team composition.

[0358] The terminal notifies participants of the optimized team composition sent from the server. After the game, it also provides feedback with advice for skill improvement based on emotional changes. This allows participants to receive specific advice that helps them improve their play style.

[0359] For example, if player A shows an expression of frustration during a game, the emotion engine analyzes this information and sends it to the server. The server combines this information with behavioral data to suggest a team composition that will help player A relax. Furthermore, the device improves the quality of the game experience by providing advice to player A, such as "Take a deep breath."

[0360] An example of a prompt message might be a request to "design a system that analyzes the emotions of game participants in real time and provides the optimal team composition based on that analysis."

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The user acquires visual and audio data of the game field and participants in real time through a recording device. Inputs include participants' movements, facial expressions, and voice tone captured by a high-precision camera and microphone. This results in output of participant behavioral information and audio information.

[0364] Step 2:

[0365] The server inputs visual and audio data transmitted from the camera into the emotion engine. The emotion engine analyzes this data using a generative AI model to infer the participant's emotional information. Based on the input data, the emotion engine performs facial recognition algorithms and audio analysis, generating data indicating the participant's emotional state as output.

[0366] Step 3:

[0367] The server integrates emotional information obtained from the emotion engine with behavioral information from the camera. It uses emotional and behavioral data as input and performs data analysis to integrate them through recording in a database. As a result, a skill score that takes the player's psychological state into account is output.

[0368] Step 4:

[0369] The server creates optimal team compositions based on the generated skill scores. The input is each participant's skill score, and an optimization algorithm is used to create teams that balance participants' mental state and abilities. The output is an optimized team composition for each player.

[0370] Step 5:

[0371] The terminal notifies participants of optimized team formations created by the server and advice for skill improvement. The input is team formation information and feedback advice from the server, and the output is a notification message sent to the participants. For example, the terminal might display the advice, "You should take some deep breaths to relax."

[0372] Step 6:

[0373] The server uses participant emotional data to suggest cooperative field matching. It uses participant emotional and skill data as input and analyzes compatibility using a generative AI model. This process produces recommended matching to facilitate trust building among participants.

[0374] (Application Example 2)

[0375] 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."

[0376] Current online content distribution services struggle to provide interactive experiences that reflect viewers' emotions and psychological states in real time, thus failing to maximize viewer satisfaction and immersion in the content.

[0377] 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.

[0378] In this invention, the server includes means for analyzing participant information and quantifying participants' behavior and performance, means for recognizing participants' emotional states in real time and adjusting interactive content, and means for providing personalized content suggestions to each viewer based on emotional data. This makes it possible to provide each viewer with an optimal content experience based on their emotions.

[0379] A "recording device" is a device used to collect participants' visual and audio data in real time and to monitor their emotions and behavior.

[0380] "Analysis" is the process of quantifying and judging participants' behavior and emotional states based on collected visual and auditory data.

[0381] "Quantification" is the process of expressing information such as participants' behavior, performance, and emotional state as quantitative data.

[0382] "Methods for optimizing team composition" refer to methods that use participants' behavioral data and emotional information to determine the most efficient and collaborative team structure.

[0383] "Means of notification" refers to communication methods used to inform participants of the results of optimized team formations and content adjustments.

[0384] "Emotional state" refers to the psychological and emotional responses exhibited by participants, which can be inferred from changes in facial expressions and voice.

[0385] "Interactive content" is content that changes in response to participants' reactions and emotions, and is an element that dynamically personalizes the viewing experience.

[0386] "Personalized content suggestions" is a feature that recommends content deemed most suitable for each participant based on their emotional data.

[0387] To implement this invention, the following system configuration is introduced.

[0388] First, the server receives visual and audio data from the recording device in order to process the data collected from numerous participants. The recording device uses devices such as smart glasses to collect participants' facial expressions and voice tone in real time. This information is analyzed through an emotion engine, and the participants' emotional state is quantified. The emotion engine uses machine learning models, and software such as OpenCV and TensorFlow are used here.

[0389] Next, the server integrates the analyzed emotional and behavioral data to tailor content to each participant's psychological state. The server runs on a cloud service (e.g., AWS Lambda) to efficiently process and analyze viewer data. Based on viewer emotional data, it interactively provides personalized content suggestions. This process uses prompts generated by a generative AI model, such as, "Analyze the viewer's facial expressions while they are watching the movie and suggest personalized content appropriate to the situation. If the viewer's emotions change, suggest what content would be appropriate."

[0390] Finally, the device receives instructions from the server and notifies participants of this feedback and adjusted content. The device uses common devices such as smartphones and tablets, providing an environment where participants can easily receive information. For example, if a participant feels tense while watching a suspense movie, the system will suggest a comical scene to adjust the viewing experience for the participant to be more comfortable.

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The user wears smart glasses, and visual and audio data are collected by a recording device. The input is real-time facial expressions and voice, and the output is data sent to an emotion engine. At this stage, concrete actions are taken to accurately capture the user's facial movements and voice tone.

[0394] Step 2:

[0395] The server analyzes the visual and auditory data received via the emotion engine to determine the participant's emotional state. The input is the output data from step 1, and the output is numerical data of the analyzed emotion. Specifically, a generative AI model is used, and emotional characteristics are quantified using OpenCV and TensorFlow.

[0396] Step 3:

[0397] The server integrates quantified sentiment data with other behavioral data to determine the optimal content adjustment or team composition for each individual user. Inputs are numerical sentiment data and behavioral history, while outputs are team composition and content suggestions. Specifically, data analysis and information integration are performed on a cloud service to determine specific recommended actions.

[0398] Step 4:

[0399] The device notifies the user of suggestions and feedback sent from the server. The input is the suggestion data obtained in step 3, and the output is the notifications and messages the user receives. Specifically, it provides an environment where participants can immediately check the information by displaying notifications on the screen of their smartphone or tablet.

[0400] Step 5:

[0401] Based on notifications from their device, users select content that matches their emotions and continue their viewing experience. The input is the notification content from the device, and the output is the content selected by the user. For example, a specific action could be the user tapping the screen to switch to comical content suggested during a tense scene.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] The system in this invention aims to create a fair and enjoyable survival game by collecting participants' battle results in real time and providing optimal team formations based on that data.

[0419] The system uses a recording device equipped with multiple sensors and cameras to track participants' movements in detail. The recording device is mounted on a platform such as a drone above the field, recording each player's position and actions. This data is transmitted to a server in real time.

[0420] The server receives data transmitted from the camera and uses image recognition technology to analyze each player's actions. This analysis quantifies the player's behavioral characteristics and performance, and calculates their skill level. This information is stored in a database and managed as the player's profile.

[0421] The server uses the acquired skill data to propose team compositions that take into account the overall balance of participants. Specifically, it considers the skills and characteristics of each player to calculate a team composition that balances the competitiveness of each team. This team composition proposal is notified to each player via their device before the game starts.

[0422] After the game, the server provides feedback to the participants. This feedback includes specific advice to help players improve their skills. For example, "To improve your accuracy, you need to aim more quickly."

[0423] Furthermore, by utilizing accumulated data, the server conducts field matching between players and proposes approaches to deepen cooperative relationships. This allows participants to improve their skills while engaging in deeper communication.

[0424] For example, if a regular meeting has many new participants, the system will use their average performance to appropriately assign experienced players to help them improve and ensure fairness in the game. Through this entire process, players can enjoy a fair and competitive gaming experience.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] A drone uses a camera to film players on the game field. The captured video data is transmitted to a server in real time.

[0428] Step 2:

[0429] The server analyzes the received video data frame by frame. Using image recognition technology, it identifies each player and extracts their location information and movement patterns.

[0430] Step 3:

[0431] The server uses the analyzed data to quantify each player's behavior patterns. Specifically, it calculates the player's movement distance, firing frequency, and hit rate to determine their skill score.

[0432] Step 4:

[0433] The server stores each player's skill score in a database and updates each player's battle record. This allows for centralized management of each player's skill level and history.

[0434] Step 5:

[0435] The server uses accumulated skill data to run an algorithm that proposes balanced team compositions. Teams are formed to minimize skill differences between players.

[0436] Step 6:

[0437] The device receives optimized team information sent from the server and notifies each player. The notification includes information about the team the player belongs to and an overview of the game rules.

[0438] Step 7:

[0439] Users participate in the game and, after it ends, receive feedback generated by the server. This feedback includes specific advice for improving their skills.

[0440] Step 8:

[0441] The server utilizes accumulated player data to suggest field matching that maximizes cooperation between players. This facilitates smoother communication and improves skills.

[0442] (Example 1)

[0443] 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."

[0444] In current survival games, differences in participants' skill levels can compromise the fairness of the game. Therefore, there is a need for an automated, real-time system that creates balanced teams that participants can enjoy. Furthermore, there is insufficient mechanism for facilitating post-game feedback and building relationships among participants.

[0445] 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.

[0446] In this invention, the server includes means for receiving data from a measuring device to capture participants' behavior, means for analyzing the received data and quantifying participants' behavioral characteristics and performance, means for optimizing team formation considering the overall balance of participants based on the quantified behavioral information, and means for notifying participants of the optimized team formation via an information terminal. This improves the fairness of the game and enables an optimal game experience tailored to each participant's skill level. Furthermore, the feedback provided after the game allows participants to obtain specific guidance for improving their skills.

[0447] "Participant" refers to an individual player who uses the system to participate in a survival game.

[0448] A "measuring device" refers to a device equipped with multiple sensors and cameras used to track the location and movements of participants in real time.

[0449] "Means of receiving data" refers to communication protocols and hardware used to transfer information from measuring devices to a server for analysis.

[0450] "Behavioral characteristics" refer to detailed behavioral patterns of participants during a game, such as their actions, reactions, and strategic decisions.

[0451] "Methods for quantifying performance" refer to algorithms and software that express participants' behavioral characteristics as quantitative data and calculate their skill levels within the game.

[0452] "Methods for optimizing team composition" refer to algorithms or programs that generate the optimal team composition to maintain fairness in the game, based on the skills and characteristics of the participants.

[0453] "Information terminal" refers to a device or interface used by participants to form teams and receive feedback before the game starts.

[0454] "Feedback" refers to specific advice and guidelines provided after a game to help participants improve their skills and performance.

[0455] The system in this invention provides advanced technology to ensure participant skill and game fairness in survival games. A server plays a central role, analyzing participant data obtained from measuring devices and forming appropriate teams.

[0456] The server uses a measurement device equipped with multiple sensors and cameras mounted on a drone to track the location and movements of participants in the field in detail. This measurement device collects real-time data from participants and transmits it to the server via an internet connection. The server uses image recognition libraries such as TensorFlow and OpenCV to analyze this data and quantify the participants' behavioral characteristics and performance.

[0457] Based on quantified data, the server uses a generative AI model to create balanced team compositions. Specifically, it calculates team compositions that balance the competitiveness of each participant, taking into account their individual skills. These team compositions are then communicated to participants via their devices before the game begins.

[0458] After the game ends, the server generates feedback based on the analysis results to help participants improve their skills. This feedback includes specific analysis results and recommended training methods to enhance the players' abilities.

[0459] Furthermore, the server proposes a field matching strategy to promote engagement and relationship building among participants. It utilizes participants' past performance data to optimize member placement for future game events. This process deepens cooperation among participants and improves the overall game experience.

[0460] An example of a prompt message might be, "In a regular game event with many new participants, please analyze each player's skill data and propose team compositions that appropriately place experienced players." This system allows participants to enjoy a fair and enjoyable gaming experience based on well-analyzed data.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The server receives real-time data from the measuring devices. The input consists of participant location and movement data acquired by multiple sensors and cameras. This data is retrieved via the network and converted into a format that can be processed within the system. Specifically, the data is passed to the analysis module in JSON format.

[0464] Step 2:

[0465] The server analyzes the received data using an image recognition library. The input consists of location data and motion data obtained in step 1. The server uses this data to quantify the participants' behavioral characteristics and performance. Specifically, it uses TensorFlow to recognize movements and gestures and calculates the players' skill items (such as hit rate and evasion ability). This process generates quantified skill data as output.

[0466] Step 3:

[0467] The server uses a generative AI model to optimize team composition based on quantified behavioral data. The input is the skill data for each participant obtained in step 2. The server uses the AI ​​model to automatically generate team compositions that ensure equal competitiveness, taking into account the characteristics of each player. The output of this process is the optimized team composition proposal.

[0468] Step 4:

[0469] The server notifies participants of the generated team composition via their terminals. The input is the proposed team composition created in step 3. This composition information is sent to the user's terminal and displayed as visually verifiable information. Specifically, a notification message is displayed on the terminal's screen, informing participants of the team information. This output is the team composition result notified to each player.

[0470] Step 5:

[0471] After the game ends, the server provides feedback to the participants. The input consists of behavioral characteristics and player performance data from Step 2. The server generates feedback from this data to help improve skills and sends it to the participants. Specific examples include advice such as, "To improve your accuracy, you should practice reflexes." The output is an individual feedback message.

[0472] Step 6:

[0473] The server proposes field matching for the next event based on game history data. The input is data showing past game performance and cooperative relationships. The server combines this information to generate new matching proposals. Specifically, it analyzes past performance data and proposes new team formations for players. This output is the field matching proposal to be used in the next game event.

[0474] (Application Example 1)

[0475] 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."

[0476] In traditional brick-and-mortar stores, it has been difficult to provide services based on the diverse purchasing trends of customers. This has resulted in the inability to offer personalized shopping experiences for each customer, hindering improvements in customer satisfaction.

[0477] 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.

[0478] In this invention, the server includes means for analyzing participant information collected by a camera and optimizing purchasing trends based on the analysis; means for notifying customers of the optimized purchasing trends; and means for storing the analyzed participant behavior information as a record and providing advice to improve the customer's purchasing experience. This makes it possible to provide personalized information to each customer in physical stores and to increase customer satisfaction.

[0479] A "recording device" is a device used to record participants' actions and performance, and is equipped with sensors and cameras.

[0480] "Participant information" refers to data collected by the camera, including information such as participants' behavior and purchasing tendencies.

[0481] "Analysis" refers to the process of analyzing data based on collected participant information and quantifying participants' behavior and performance.

[0482] "Quantification" is the process of converting participants' behavior and performance into quantitative data.

[0483] "Methods for optimizing purchasing trends" refer to the process of generating information to provide individual customers with the most optimal purchasing experience, based on quantified participant behavioral data.

[0484] "Means of notifying customers" refers to methods of delivering information to customers based on optimized purchasing trends, including smartphone applications and other communication methods.

[0485] "Storing as a record" refers to the act of saving the analyzed participant behavior information in a database or similar system for future use.

[0486] "Means of providing advice" refers to methods of providing customers with personalized purchasing advice and information based on stored data.

[0487] In this invention, the server constructs a system for collecting and analyzing user purchasing behavior. The hardware used includes sensors and cameras within the store, as well as the user's smartphone. The sensors and cameras record the movement and purchasing behavior of participants (customers) in real time, and the smartphone supplements this data using GPS and Bluetooth functions.

[0488] The collected data is sent to a server built within the cloud service. On the server, this data is analyzed using an AI model to quantify and optimize customer purchasing trends. Machine learning algorithms are applied to the analysis to generate personalized product recommendations and campaign information for each customer.

[0489] The generated suggestions and information are notified through an application installed on the customer's smartphone. These notifications include special offers, promotions, and product recommendations tailored to the customer's interests. This enhances the user's in-store shopping experience and increases their satisfaction.

[0490] For example, if a customer is interested in a vegan diet and frequently purchases vegan-related products based on their past purchase history, they will receive information about new products and promotions tailored to that trend. The system also uses prompts like the following to input data into an AI model and generate effective suggestions: "Based on past purchase data, please suggest the best products for this customer. Since they have purchased vegan products in the past, they are likely interested in vegan-related products. Please consider recently added new products."

[0491] This format enables effective customer engagement and personalized services in physical stores, leading to improved operational efficiency and user experience.

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The server collects customer behavior data using sensors and cameras within the store. This data includes customer movement patterns, dwell time, and interest in specific products. Input is raw data from sensors and cameras, and output is stored in a database as individual customer behavior information.

[0495] Step 2:

[0496] The terminal (smartphone) uses Bluetooth and GPS functions to acquire customer location information. It receives real-time location data sent from the smartphone as input and sends information to the server that visualizes the customer's movements and current location within the store as output.

[0497] Step 3:

[0498] The server uses an AI model to analyze customer purchasing trends based on collected behavioral data. The input is stored customer behavior data, and the output generates an optimal product list and purchase recommendations for each customer. The AI ​​model receives data based on prompt messages, generating accurate product suggestions.

[0499] Step 4:

[0500] The server notifies the customer's device of the generated purchase suggestions. Using the generated product list and suggestion content as input, the output is a notification message displayed on the customer's smartphone. This allows customers to receive information based on their interests in real time.

[0501] Step 5:

[0502] Users (customers) receive information notified on their smartphones and make purchasing decisions based on that information. The input is the information displayed on the smartphone, and the output is a change in their purchasing behavior within the store. This leads to improved customer satisfaction.

[0503] 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.

[0504] This invention provides a system that recognizes participants' emotions and uses that information to improve the quality of the game experience. The system consists of a camera, an emotion engine, an analysis server, and a user terminal.

[0505] The camera monitors the game field and participants, collecting visual data in real time. This allows for the acquisition of basic information about players' movements and actions.

[0506] The emotion engine analyzes acquired visual data and audio input to infer emotions from participants' facial expressions and tone of voice. This emotion data indicates the player's psychological state during the game.

[0507] The server comprehensively analyzes emotional data transmitted from the emotion engine and behavioral data obtained from the camera. This information is combined with each player's skill evaluation and performance during the game to generate a more detailed skill score.

[0508] Furthermore, the server uses the collected data to create teams that take into account the players' psychological state. For example, it might pair a nervous player with an experienced collaborator to help them relax. This kind of team composition, which considers psychological factors, aims to improve players' performance in the game.

[0509] The device not only sends players notifications of team composition based on analysis results, but also provides post-game feedback, including advice for skill improvement based on the player's emotional changes. For example, it may include specific advice such as, "Positive feedback can boost self-efficacy and lead to improved performance."

[0510] Furthermore, the server utilizes emotional data to suggest field matches that are expected to facilitate smooth communication. This process makes it easier for participants to build trust with each other, resulting in a cooperative and enriching gaming experience.

[0511] For example, if player A becomes frustrated during a game, the emotion engine detects the change in their facial expression and sends emotional data. The server analyzes this data and, in the next team formation, suggests teaming player A with player B, who is good at entertaining others, in order to alleviate their mood. In this way, the use of the emotion engine dramatically improves participant satisfaction in the game experience.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The recording equipment constantly monitors the entire game field, recording participants' movements and facial expressions in real time. This allows for the acquisition of visual and audio data.

[0515] Step 2:

[0516] The emotion engine analyzes visual and auditory data to infer the emotional state of participants based on their facial expressions and tone of voice. This analysis result is sent to the server as emotion data.

[0517] Step 3:

[0518] The server simultaneously processes emotional data and behavioral data transmitted from the recording device, and calculates a skill score that integrates emotional information into each player's skill evaluation. This creates a comprehensive player profile that takes psychological factors into account.

[0519] Step 4:

[0520] The server optimizes team composition based on the acquired skill scores, tailoring it to each player's psychological state. Teams are formed to reflect players' levels of tension and friendliness, and adjusted to achieve the most positive results.

[0521] Step 5:

[0522] The terminal receives optimized team information calculated by the server and notifies each player. The notification includes the changed team members and their roles.

[0523] Step 6:

[0524] Users review the notified information and start the game as their assigned team. Team formation based on sentiment data promotes smooth communication and cooperative gameplay.

[0525] Step 7:

[0526] After the game ends, the server provides feedback to the participants. This feedback includes specific advice for skill improvement based on emotional changes and performance results. Users can use this to prepare for their next game.

[0527] Step 8:

[0528] The server uses collected emotional data to analyze how well certain players are compatible with each other and makes suggestions for future field matching. This allows for continuous improvement that takes emotional compatibility into account.

[0529] (Example 2)

[0530] 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."

[0531] Traditional game systems collect and analyze data without considering participants' emotions or psychological states, resulting in team formation and skill improvement advice that did not adequately contribute to participant satisfaction. Therefore, there is a need for optimized team formation that reflects the diverse psychological states of participants, as well as feedback and skill improvement advice based on emotional changes.

[0532] 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.

[0533] In this invention, the server includes means for analyzing participant information collected by a camera, means for integrating quantified participant behavioral and emotional information to generate a skill score that takes into account the player's psychological state, and means for notifying participants of the optimized team composition and providing advice for skill improvement based on emotional changes. This makes it possible to optimize team composition based on the psychological state of each participant and to provide appropriate feedback and advice for skill improvement that utilizes the emotional changes of the participants.

[0534] A "camera equipment" is a device used to monitor the game field and participants in real time and to collect visual and audio data.

[0535] "Participant information" refers to data such as participants' behavior, facial expressions, and voice collected by the recording device.

[0536] "Analysis" is the act of interpreting the meaning of data using a specific algorithm based on participant information and quantifying it.

[0537] "Quantification" is the process of expressing data obtained through analysis as objective numerical values.

[0538] "Behavioral information" refers to data about participants' actions, such as their movements and play style.

[0539] "Emotional information" refers to data on the emotional state inferred from the participants' facial expressions and tone of voice.

[0540] A "skill score" is a numerical value that indicates an individual's ability, calculated from the combined results of participants' behavioral and emotional information.

[0541] "Team formation" is the process of combining multiple participants to create a team.

[0542] "Feedback" refers to advice and comments provided to improve performance, based on the emotional changes of participants.

[0543] "Skill improvement advice" refers to specific advice and guidance provided to enhance participants' abilities.

[0544] "Psychological state" refers to the mental state or mental tendencies interpreted from the emotional information of the participants.

[0545] This invention is a system that recognizes participants' emotions and uses that information to improve the quality of the game experience. The system consists of a camera, an emotion engine, an analysis server, and a user terminal.

[0546] The user collects real-time visual data of the game field and participants using a recording device. This device incorporates a high-precision camera and microphone to record players' movements, facial expressions, and voice tones in detail.

[0547] The server transmits data acquired from the camera to the emotion engine. The emotion engine uses machine learning algorithms to analyze the visual and audio data to infer the participants' emotional information. This emotional information includes the various emotional states that the participants experience during the game.

[0548] Through collaboration between the emotion engine and the server, the server integrates participant behavioral and emotional information. This generates a skill score that takes into account the player's psychological state. This skill score is used to optimize team composition.

[0549] The terminal notifies participants of the optimized team composition sent from the server. After the game, it also provides feedback with advice for skill improvement based on emotional changes. This allows participants to receive specific advice that helps them improve their play style.

[0550] For example, if player A shows an expression of frustration during a game, the emotion engine analyzes this information and sends it to the server. The server combines this information with behavioral data to suggest a team composition that will help player A relax. Furthermore, the device improves the quality of the game experience by providing advice to player A, such as "Take a deep breath."

[0551] An example of a prompt message might be a request to "design a system that analyzes the emotions of game participants in real time and provides the optimal team composition based on that analysis."

[0552] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0553] Step 1:

[0554] The user acquires visual and audio data of the game field and participants in real time through a recording device. Inputs include participants' movements, facial expressions, and voice tone captured by a high-precision camera and microphone. This results in output of participant behavioral information and audio information.

[0555] Step 2:

[0556] The server inputs visual and audio data transmitted from the camera into the emotion engine. The emotion engine analyzes this data using a generative AI model to infer the participant's emotional information. Based on the input data, the emotion engine performs facial recognition algorithms and audio analysis, generating data indicating the participant's emotional state as output.

[0557] Step 3:

[0558] The server integrates emotional information obtained from the emotion engine with behavioral information from the camera. It uses emotional and behavioral data as input and performs data analysis to integrate them through recording in a database. As a result, a skill score that takes the player's psychological state into account is output.

[0559] Step 4:

[0560] The server creates optimal team compositions based on the generated skill scores. The input is each participant's skill score, and an optimization algorithm is used to create teams that balance participants' mental state and abilities. The output is an optimized team composition for each player.

[0561] Step 5:

[0562] The terminal notifies participants of optimized team formations created by the server and advice for skill improvement. The input is team formation information and feedback advice from the server, and the output is a notification message sent to the participants. For example, the terminal might display the advice, "You should take some deep breaths to relax."

[0563] Step 6:

[0564] The server uses participant emotional data to suggest cooperative field matching. It uses participant emotional and skill data as input and analyzes compatibility using a generative AI model. This process produces recommended matching to facilitate trust building among participants.

[0565] (Application Example 2)

[0566] 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."

[0567] Current online content distribution services struggle to provide interactive experiences that reflect viewers' emotions and psychological states in real time, thus failing to maximize viewer satisfaction and immersion in the content.

[0568] 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.

[0569] In this invention, the server includes means for analyzing participant information and quantifying participants' behavior and performance, means for recognizing participants' emotional states in real time and adjusting interactive content, and means for providing personalized content suggestions to each viewer based on emotional data. This makes it possible to provide each viewer with an optimal content experience based on their emotions.

[0570] A "recording device" is a device used to collect participants' visual and audio data in real time and to monitor their emotions and behavior.

[0571] "Analysis" is the process of quantifying and judging participants' behavior and emotional states based on collected visual and auditory data.

[0572] "Quantification" is the process of expressing information such as participants' behavior, performance, and emotional state as quantitative data.

[0573] "Methods for optimizing team composition" refer to methods that use participants' behavioral data and emotional information to determine the most efficient and collaborative team structure.

[0574] "Means of notification" refers to communication methods used to inform participants of the results of optimized team formations and content adjustments.

[0575] "Emotional state" refers to the psychological and emotional responses exhibited by participants, which can be inferred from changes in facial expressions and voice.

[0576] "Interactive content" is content that changes in response to participants' reactions and emotions, and is an element that dynamically personalizes the viewing experience.

[0577] "Personalized content suggestions" is a feature that recommends content deemed most suitable for each participant based on their emotional data.

[0578] To implement this invention, the following system configuration is introduced.

[0579] First, the server receives visual and audio data from the recording device in order to process the data collected from numerous participants. The recording device uses devices such as smart glasses to collect participants' facial expressions and voice tone in real time. This information is analyzed through an emotion engine, and the participants' emotional state is quantified. The emotion engine uses machine learning models, and software such as OpenCV and TensorFlow are used here.

[0580] Next, the server integrates the analyzed emotional and behavioral data to tailor content to each participant's psychological state. The server runs on a cloud service (e.g., AWS Lambda) to efficiently process and analyze viewer data. Based on viewer emotional data, it interactively provides personalized content suggestions. This process uses prompts generated by a generative AI model, such as, "Analyze the viewer's facial expressions while they are watching the movie and suggest personalized content appropriate to the situation. If the viewer's emotions change, suggest what content would be appropriate."

[0581] Finally, the device receives instructions from the server and notifies participants of this feedback and adjusted content. The device uses common devices such as smartphones and tablets, providing an environment where participants can easily receive information. For example, if a participant feels tense while watching a suspense movie, the system will suggest a comical scene to adjust the viewing experience for the participant to be more comfortable.

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The user wears smart glasses, and visual and audio data are collected by a recording device. The input is real-time facial expressions and voice, and the output is data sent to an emotion engine. At this stage, concrete actions are taken to accurately capture the user's facial movements and voice tone.

[0585] Step 2:

[0586] The server analyzes the visual and auditory data received via the emotion engine to determine the participant's emotional state. The input is the output data from step 1, and the output is numerical data of the analyzed emotion. Specifically, a generative AI model is used, and emotional characteristics are quantified using OpenCV and TensorFlow.

[0587] Step 3:

[0588] The server integrates quantified sentiment data with other behavioral data to determine the optimal content adjustment or team composition for each individual user. Inputs are numerical sentiment data and behavioral history, while outputs are team composition and content suggestions. Specifically, data analysis and information integration are performed on a cloud service to determine specific recommended actions.

[0589] Step 4:

[0590] The device notifies the user of suggestions and feedback sent from the server. The input is the suggestion data obtained in step 3, and the output is the notifications and messages the user receives. Specifically, it provides an environment where participants can immediately check the information by displaying notifications on the screen of their smartphone or tablet.

[0591] Step 5:

[0592] Based on notifications from their device, users select content that matches their emotions and continue their viewing experience. The input is the notification content from the device, and the output is the content selected by the user. For example, a specific action could be the user tapping the screen to switch to comical content suggested during a tense scene.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] [Fourth Embodiment]

[0597] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0598] 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.

[0599] 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).

[0600] 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.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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".

[0610] The system in this invention aims to create a fair and enjoyable survival game by collecting participants' battle results in real time and providing optimal team formations based on that data.

[0611] The system uses a recording device equipped with multiple sensors and cameras to track participants' movements in detail. The recording device is mounted on a platform such as a drone above the field, recording each player's position and actions. This data is transmitted to a server in real time.

[0612] The server receives data transmitted from the camera and uses image recognition technology to analyze each player's actions. This analysis quantifies the player's behavioral characteristics and performance, and calculates their skill level. This information is stored in a database and managed as the player's profile.

[0613] The server uses the acquired skill data to propose team compositions that take into account the overall balance of participants. Specifically, it considers the skills and characteristics of each player to calculate a team composition that balances the competitiveness of each team. This team composition proposal is notified to each player via their device before the game starts.

[0614] After the game, the server provides feedback to the participants. This feedback includes specific advice to help players improve their skills. For example, "To improve your accuracy, you need to aim more quickly."

[0615] Furthermore, by utilizing accumulated data, the server conducts field matching between players and proposes approaches to deepen cooperative relationships. This allows participants to improve their skills while engaging in deeper communication.

[0616] For example, if a regular meeting has many new participants, the system will use their average performance to appropriately assign experienced players to help them improve and ensure fairness in the game. Through this entire process, players can enjoy a fair and competitive gaming experience.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] A drone uses a camera to film players on the game field. The captured video data is transmitted to a server in real time.

[0620] Step 2:

[0621] The server analyzes the received video data frame by frame. Using image recognition technology, it identifies each player and extracts their location information and movement patterns.

[0622] Step 3:

[0623] The server uses the analyzed data to quantify each player's behavior patterns. Specifically, it calculates the player's movement distance, firing frequency, and hit rate to determine their skill score.

[0624] Step 4:

[0625] The server stores each player's skill score in a database and updates each player's battle record. This allows for centralized management of each player's skill level and history.

[0626] Step 5:

[0627] The server uses accumulated skill data to run an algorithm that proposes balanced team compositions. Teams are formed to minimize skill differences between players.

[0628] Step 6:

[0629] The device receives optimized team information sent from the server and notifies each player. The notification includes information about the team the player belongs to and an overview of the game rules.

[0630] Step 7:

[0631] Users participate in the game and, after it ends, receive feedback generated by the server. This feedback includes specific advice for improving their skills.

[0632] Step 8:

[0633] The server utilizes accumulated player data to suggest field matching that maximizes cooperation between players. This facilitates smoother communication and improves skills.

[0634] (Example 1)

[0635] 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".

[0636] In current survival games, differences in participants' skill levels can compromise the fairness of the game. Therefore, there is a need for an automated, real-time system that creates balanced teams that participants can enjoy. Furthermore, there is insufficient mechanism for facilitating post-game feedback and building relationships among participants.

[0637] 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.

[0638] In this invention, the server includes means for receiving data from a measuring device to capture participants' behavior, means for analyzing the received data and quantifying participants' behavioral characteristics and performance, means for optimizing team formation considering the overall balance of participants based on the quantified behavioral information, and means for notifying participants of the optimized team formation via an information terminal. This improves the fairness of the game and enables an optimal game experience tailored to each participant's skill level. Furthermore, the feedback provided after the game allows participants to obtain specific guidance for improving their skills.

[0639] "Participant" refers to an individual player who uses the system to participate in a survival game.

[0640] A "measuring device" refers to a device equipped with multiple sensors and cameras used to track the location and movements of participants in real time.

[0641] "Means of receiving data" refers to communication protocols and hardware used to transfer information from measuring devices to a server for analysis.

[0642] "Behavioral characteristics" refer to detailed behavioral patterns of participants during a game, such as their actions, reactions, and strategic decisions.

[0643] "Methods for quantifying performance" refer to algorithms and software that express participants' behavioral characteristics as quantitative data and calculate their skill levels within the game.

[0644] "Methods for optimizing team composition" refer to algorithms or programs that generate the optimal team composition to maintain fairness in the game, based on the skills and characteristics of the participants.

[0645] "Information terminal" refers to a device or interface used by participants to form teams and receive feedback before the game starts.

[0646] "Feedback" refers to specific advice and guidelines provided after a game to help participants improve their skills and performance.

[0647] The system in this invention provides advanced technology to ensure participant skill and game fairness in survival games. A server plays a central role, analyzing participant data obtained from measuring devices and forming appropriate teams.

[0648] The server uses a measurement device equipped with multiple sensors and cameras mounted on a drone to track the location and movements of participants in the field in detail. This measurement device collects real-time data from participants and transmits it to the server via an internet connection. The server uses image recognition libraries such as TensorFlow and OpenCV to analyze this data and quantify the participants' behavioral characteristics and performance.

[0649] Based on quantified data, the server uses a generative AI model to create balanced team compositions. Specifically, it calculates team compositions that balance the competitiveness of each participant, taking into account their individual skills. These team compositions are then communicated to participants via their devices before the game begins.

[0650] After the game ends, the server generates feedback based on the analysis results to help participants improve their skills. This feedback includes specific analysis results and recommended training methods to enhance the players' abilities.

[0651] Furthermore, the server proposes a field matching strategy to promote engagement and relationship building among participants. It utilizes participants' past performance data to optimize member placement for future game events. This process deepens cooperation among participants and improves the overall game experience.

[0652] An example of a prompt message might be, "In a regular game event with many new participants, please analyze each player's skill data and propose team compositions that appropriately place experienced players." This system allows participants to enjoy a fair and enjoyable gaming experience based on well-analyzed data.

[0653] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0654] Step 1:

[0655] The server receives real-time data from the measuring devices. The input consists of participant location and movement data acquired by multiple sensors and cameras. This data is retrieved via the network and converted into a format that can be processed within the system. Specifically, the data is passed to the analysis module in JSON format.

[0656] Step 2:

[0657] The server analyzes the received data using an image recognition library. The input consists of location data and motion data obtained in step 1. The server uses this data to quantify the participants' behavioral characteristics and performance. Specifically, it uses TensorFlow to recognize movements and gestures and calculates the players' skill items (such as hit rate and evasion ability). This process generates quantified skill data as output.

[0658] Step 3:

[0659] The server uses a generative AI model to optimize team composition based on quantified behavioral data. The input is the skill data for each participant obtained in step 2. The server uses the AI ​​model to automatically generate team compositions that ensure equal competitiveness, taking into account the characteristics of each player. The output of this process is the optimized team composition proposal.

[0660] Step 4:

[0661] The server notifies participants of the generated team composition via their terminals. The input is the proposed team composition created in step 3. This composition information is sent to the user's terminal and displayed as visually verifiable information. Specifically, a notification message is displayed on the terminal's screen, informing participants of the team information. This output is the team composition result notified to each player.

[0662] Step 5:

[0663] After the game ends, the server provides feedback to the participants. The input consists of behavioral characteristics and player performance data from Step 2. The server generates feedback from this data to help improve skills and sends it to the participants. Specific examples include advice such as, "To improve your accuracy, you should practice reflexes." The output is an individual feedback message.

[0664] Step 6:

[0665] The server proposes field matching for the next event based on game history data. The input is data showing past game performance and cooperative relationships. The server combines this information to generate new matching proposals. Specifically, it analyzes past performance data and proposes new team formations for players. This output is the field matching proposal to be used in the next game event.

[0666] (Application Example 1)

[0667] 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".

[0668] In traditional brick-and-mortar stores, it has been difficult to provide services based on the diverse purchasing trends of customers. This has resulted in the inability to offer personalized shopping experiences for each customer, hindering improvements in customer satisfaction.

[0669] 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.

[0670] In this invention, the server includes means for analyzing participant information collected by a camera and optimizing purchasing trends based on the analysis; means for notifying customers of the optimized purchasing trends; and means for storing the analyzed participant behavior information as a record and providing advice to improve the customer's purchasing experience. This makes it possible to provide personalized information to each customer in physical stores and to increase customer satisfaction.

[0671] A "recording device" is a device used to record participants' actions and performance, and is equipped with sensors and cameras.

[0672] "Participant information" refers to data collected by the camera, including information such as participants' behavior and purchasing tendencies.

[0673] "Analysis" refers to the process of analyzing data based on collected participant information and quantifying participants' behavior and performance.

[0674] "Quantification" is the process of converting participants' behavior and performance into quantitative data.

[0675] "Methods for optimizing purchasing trends" refer to the process of generating information to provide individual customers with the most optimal purchasing experience, based on quantified participant behavioral data.

[0676] "Means of notifying customers" refers to methods of delivering information to customers based on optimized purchasing trends, including smartphone applications and other communication methods.

[0677] "Storing as a record" refers to the act of saving the analyzed participant behavior information in a database or similar system for future use.

[0678] "Means of providing advice" refers to methods of providing customers with personalized purchasing advice and information based on stored data.

[0679] In this invention, the server constructs a system for collecting and analyzing user purchasing behavior. The hardware used includes sensors and cameras within the store, as well as the user's smartphone. The sensors and cameras record the movement and purchasing behavior of participants (customers) in real time, and the smartphone supplements this data using GPS and Bluetooth functions.

[0680] The collected data is sent to a server built within the cloud service. On the server, this data is analyzed using an AI model to quantify and optimize customer purchasing trends. Machine learning algorithms are applied to the analysis to generate personalized product recommendations and campaign information for each customer.

[0681] The generated suggestions and information are notified through an application installed on the customer's smartphone. These notifications include special offers, promotions, and product recommendations tailored to the customer's interests. This enhances the user's in-store shopping experience and increases their satisfaction.

[0682] For example, if a customer is interested in a vegan diet and frequently purchases vegan-related products based on their past purchase history, they will receive information about new products and promotions tailored to that trend. The system also uses prompts like the following to input data into an AI model and generate effective suggestions: "Based on past purchase data, please suggest the best products for this customer. Since they have purchased vegan products in the past, they are likely interested in vegan-related products. Please consider recently added new products."

[0683] This format enables effective customer engagement and personalized services in physical stores, leading to improved operational efficiency and user experience.

[0684] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0685] Step 1:

[0686] The server collects customer behavior data using sensors and cameras within the store. This data includes customer movement patterns, dwell time, and interest in specific products. Input is raw data from sensors and cameras, and output is stored in a database as individual customer behavior information.

[0687] Step 2:

[0688] The terminal (smartphone) uses Bluetooth and GPS functions to acquire customer location information. It receives real-time location data sent from the smartphone as input and sends information to the server that visualizes the customer's movements and current location within the store as output.

[0689] Step 3:

[0690] The server uses an AI model to analyze customer purchasing trends based on collected behavioral data. The input is stored customer behavior data, and the output generates an optimal product list and purchase recommendations for each customer. The AI ​​model receives data based on prompt messages, generating accurate product suggestions.

[0691] Step 4:

[0692] The server notifies the customer's device of the generated purchase suggestions. Using the generated product list and suggestion content as input, the output is a notification message displayed on the customer's smartphone. This allows customers to receive information based on their interests in real time.

[0693] Step 5:

[0694] Users (customers) receive information notified on their smartphones and make purchasing decisions based on that information. The input is the information displayed on the smartphone, and the output is a change in their purchasing behavior within the store. This leads to improved customer satisfaction.

[0695] 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.

[0696] This invention provides a system that recognizes participants' emotions and uses that information to improve the quality of the game experience. The system consists of a camera, an emotion engine, an analysis server, and a user terminal.

[0697] The camera monitors the game field and participants, collecting visual data in real time. This allows for the acquisition of basic information about players' movements and actions.

[0698] The emotion engine analyzes acquired visual data and audio input to infer emotions from participants' facial expressions and tone of voice. This emotion data indicates the player's psychological state during the game.

[0699] The server comprehensively analyzes emotional data transmitted from the emotion engine and behavioral data obtained from the camera. This information is combined with each player's skill evaluation and performance during the game to generate a more detailed skill score.

[0700] Furthermore, the server uses the collected data to create teams that take into account the players' psychological state. For example, it might pair a nervous player with an experienced collaborator to help them relax. This kind of team composition, which considers psychological factors, aims to improve players' performance in the game.

[0701] The device not only sends players notifications of team composition based on analysis results, but also provides post-game feedback, including advice for skill improvement based on the player's emotional changes. For example, it may include specific advice such as, "Positive feedback can boost self-efficacy and lead to improved performance."

[0702] Furthermore, the server utilizes emotional data to suggest field matches that are expected to facilitate smooth communication. This process makes it easier for participants to build trust with each other, resulting in a cooperative and enriching gaming experience.

[0703] For example, if player A becomes frustrated during a game, the emotion engine detects the change in their facial expression and sends emotional data. The server analyzes this data and, in the next team formation, suggests teaming player A with player B, who is good at entertaining others, in order to alleviate their mood. In this way, the use of the emotion engine dramatically improves participant satisfaction in the game experience.

[0704] The following describes the processing flow.

[0705] Step 1:

[0706] The recording equipment constantly monitors the entire game field, recording participants' movements and facial expressions in real time. This allows for the acquisition of visual and audio data.

[0707] Step 2:

[0708] The emotion engine analyzes visual and auditory data to infer the emotional state of participants based on their facial expressions and tone of voice. This analysis result is sent to the server as emotion data.

[0709] Step 3:

[0710] The server simultaneously processes emotional data and behavioral data transmitted from the recording device, and calculates a skill score that integrates emotional information into each player's skill evaluation. This creates a comprehensive player profile that takes psychological factors into account.

[0711] Step 4:

[0712] The server optimizes team composition based on the acquired skill scores, tailoring it to each player's psychological state. Teams are formed to reflect players' levels of tension and friendliness, and adjusted to achieve the most positive results.

[0713] Step 5:

[0714] The terminal receives optimized team information calculated by the server and notifies each player. The notification includes the changed team members and their roles.

[0715] Step 6:

[0716] Users review the notified information and start the game as their assigned team. Team formation based on sentiment data promotes smooth communication and cooperative gameplay.

[0717] Step 7:

[0718] After the game ends, the server provides feedback to the participants. This feedback includes specific advice for skill improvement based on emotional changes and performance results. Users can use this to prepare for their next game.

[0719] Step 8:

[0720] The server uses collected emotional data to analyze how well certain players are compatible with each other and makes suggestions for future field matching. This allows for continuous improvement that takes emotional compatibility into account.

[0721] (Example 2)

[0722] 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".

[0723] Traditional game systems collect and analyze data without considering participants' emotions or psychological states, resulting in team formation and skill improvement advice that did not adequately contribute to participant satisfaction. Therefore, there is a need for optimized team formation that reflects the diverse psychological states of participants, as well as feedback and skill improvement advice based on emotional changes.

[0724] 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.

[0725] In this invention, the server includes means for analyzing participant information collected by a camera, means for integrating quantified participant behavioral and emotional information to generate a skill score that takes into account the player's psychological state, and means for notifying participants of the optimized team composition and providing advice for skill improvement based on emotional changes. This makes it possible to optimize team composition based on the psychological state of each participant and to provide appropriate feedback and advice for skill improvement that utilizes the emotional changes of the participants.

[0726] A "camera equipment" is a device used to monitor the game field and participants in real time and to collect visual and audio data.

[0727] "Participant information" refers to data such as participants' behavior, facial expressions, and voice collected by the recording device.

[0728] "Analysis" is the act of interpreting the meaning of data using a specific algorithm based on participant information and quantifying it.

[0729] "Quantification" is the process of expressing data obtained through analysis as objective numerical values.

[0730] "Behavioral information" refers to data about participants' actions, such as their movements and play style.

[0731] "Emotional information" refers to data on the emotional state inferred from the participants' facial expressions and tone of voice.

[0732] A "skill score" is a numerical value that indicates an individual's ability, calculated from the combined results of participants' behavioral and emotional information.

[0733] "Team formation" is the process of combining multiple participants to create a team.

[0734] "Feedback" refers to advice and comments provided to improve performance, based on the emotional changes of participants.

[0735] "Skill improvement advice" refers to specific advice and guidance provided to enhance participants' abilities.

[0736] "Psychological state" refers to the mental state or mental tendencies interpreted from the emotional information of the participants.

[0737] This invention is a system that recognizes participants' emotions and uses that information to improve the quality of the game experience. The system consists of a camera, an emotion engine, an analysis server, and a user terminal.

[0738] The user collects real-time visual data of the game field and participants using a recording device. This device incorporates a high-precision camera and microphone to record players' movements, facial expressions, and voice tones in detail.

[0739] The server transmits data acquired from the camera to the emotion engine. The emotion engine uses machine learning algorithms to analyze the visual and audio data to infer the participants' emotional information. This emotional information includes the various emotional states that the participants experience during the game.

[0740] Through collaboration between the emotion engine and the server, the server integrates participant behavioral and emotional information. This generates a skill score that takes into account the player's psychological state. This skill score is used to optimize team composition.

[0741] The terminal notifies participants of the optimized team composition sent from the server. After the game, it also provides feedback with advice for skill improvement based on emotional changes. This allows participants to receive specific advice that helps them improve their play style.

[0742] For example, if player A shows an expression of frustration during a game, the emotion engine analyzes this information and sends it to the server. The server combines this information with behavioral data to suggest a team composition that will help player A relax. Furthermore, the device improves the quality of the game experience by providing advice to player A, such as "Take a deep breath."

[0743] An example of a prompt message might be a request to "design a system that analyzes the emotions of game participants in real time and provides the optimal team composition based on that analysis."

[0744] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0745] Step 1:

[0746] The user acquires visual and audio data of the game field and participants in real time through a recording device. Inputs include participants' movements, facial expressions, and voice tone captured by a high-precision camera and microphone. This results in output of participant behavioral information and audio information.

[0747] Step 2:

[0748] The server inputs visual and audio data transmitted from the camera into the emotion engine. The emotion engine analyzes this data using a generative AI model to infer the participant's emotional information. Based on the input data, the emotion engine performs facial recognition algorithms and audio analysis, generating data indicating the participant's emotional state as output.

[0749] Step 3:

[0750] The server integrates emotional information obtained from the emotion engine with behavioral information from the camera. It uses emotional and behavioral data as input and performs data analysis to integrate them through recording in a database. As a result, a skill score that takes the player's psychological state into account is output.

[0751] Step 4:

[0752] The server creates optimal team compositions based on the generated skill scores. The input is each participant's skill score, and an optimization algorithm is used to create teams that balance participants' mental state and abilities. The output is an optimized team composition for each player.

[0753] Step 5:

[0754] The terminal notifies participants of optimized team formations created by the server and advice for skill improvement. The input is team formation information and feedback advice from the server, and the output is a notification message sent to the participants. For example, the terminal might display the advice, "You should take some deep breaths to relax."

[0755] Step 6:

[0756] The server uses participant emotional data to suggest cooperative field matching. It uses participant emotional and skill data as input and analyzes compatibility using a generative AI model. This process produces recommended matching to facilitate trust building among participants.

[0757] (Application Example 2)

[0758] 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".

[0759] Current online content distribution services struggle to provide interactive experiences that reflect viewers' emotions and psychological states in real time, thus failing to maximize viewer satisfaction and immersion in the content.

[0760] 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.

[0761] In this invention, the server includes means for analyzing participant information and quantifying participants' behavior and performance, means for recognizing participants' emotional states in real time and adjusting interactive content, and means for providing personalized content suggestions to each viewer based on emotional data. This makes it possible to provide each viewer with an optimal content experience based on their emotions.

[0762] A "recording device" is a device used to collect participants' visual and audio data in real time and to monitor their emotions and behavior.

[0763] "Analysis" is the process of quantifying and judging participants' behavior and emotional states based on collected visual and auditory data.

[0764] "Quantification" is the process of expressing information such as participants' behavior, performance, and emotional state as quantitative data.

[0765] "Methods for optimizing team composition" refer to methods that use participants' behavioral data and emotional information to determine the most efficient and collaborative team structure.

[0766] "Means of notification" refers to communication methods used to inform participants of the results of optimized team formations and content adjustments.

[0767] "Emotional state" refers to the psychological and emotional responses exhibited by participants, which can be inferred from changes in facial expressions and voice.

[0768] "Interactive content" is content that changes in response to participants' reactions and emotions, and is an element that dynamically personalizes the viewing experience.

[0769] "Personalized content suggestions" is a feature that recommends content deemed most suitable for each participant based on their emotional data.

[0770] To implement this invention, the following system configuration is introduced.

[0771] First, the server receives visual and audio data from the recording device in order to process the data collected from numerous participants. The recording device uses devices such as smart glasses to collect participants' facial expressions and voice tone in real time. This information is analyzed through an emotion engine, and the participants' emotional state is quantified. The emotion engine uses machine learning models, and software such as OpenCV and TensorFlow are used here.

[0772] Next, the server integrates the analyzed emotional and behavioral data to tailor content to each participant's psychological state. The server runs on a cloud service (e.g., AWS Lambda) to efficiently process and analyze viewer data. Based on viewer emotional data, it interactively provides personalized content suggestions. This process uses prompts generated by a generative AI model, such as, "Analyze the viewer's facial expressions while they are watching the movie and suggest personalized content appropriate to the situation. If the viewer's emotions change, suggest what content would be appropriate."

[0773] Finally, the device receives instructions from the server and notifies participants of this feedback and adjusted content. The device uses common devices such as smartphones and tablets, providing an environment where participants can easily receive information. For example, if a participant feels tense while watching a suspense movie, the system will suggest a comical scene to adjust the viewing experience for the participant to be more comfortable.

[0774] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0775] Step 1:

[0776] The user wears smart glasses, and visual and audio data are collected by a recording device. The input is real-time facial expressions and voice, and the output is data sent to an emotion engine. At this stage, concrete actions are taken to accurately capture the user's facial movements and voice tone.

[0777] Step 2:

[0778] The server analyzes the visual and auditory data received via the emotion engine to determine the participant's emotional state. The input is the output data from step 1, and the output is numerical data of the analyzed emotion. Specifically, a generative AI model is used, and emotional characteristics are quantified using OpenCV and TensorFlow.

[0779] Step 3:

[0780] The server integrates quantified sentiment data with other behavioral data to determine the optimal content adjustment or team composition for each individual user. Inputs are numerical sentiment data and behavioral history, while outputs are team composition and content suggestions. Specifically, data analysis and information integration are performed on a cloud service to determine specific recommended actions.

[0781] Step 4:

[0782] The device notifies the user of suggestions and feedback sent from the server. The input is the suggestion data obtained in step 3, and the output is the notifications and messages the user receives. Specifically, it provides an environment where participants can immediately check the information by displaying notifications on the screen of their smartphone or tablet.

[0783] Step 5:

[0784] Based on notifications from their device, users select content that matches their emotions and continue their viewing experience. The input is the notification content from the device, and the output is the content selected by the user. For example, a specific action could be the user tapping the screen to switch to comical content suggested during a tense scene.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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."

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] The following is further disclosed regarding the embodiments described above.

[0807] (Claim 1)

[0808] Analyze participant information collected by the imaging device,

[0809] This analysis provides a means to quantify the behavior and performance of participants,

[0810] A means to optimize team composition based on quantified participant behavioral information,

[0811] A means of notifying participants of the optimized team composition,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The analyzed participant behavior information is saved as a record.

[0815] Based on these records, including means of providing advice to improve participants' skills,

[0816] The system according to claim 1.

[0817] (Claim 3)

[0818] This includes means of providing field matching to facilitate communication among participants and strengthen collaborative relationships.

[0819] The system according to claim 1.

[0820] "Example 1"

[0821] (Claim 1)

[0822] In order to capture the behavior of participants, a means for receiving data from a measuring device,

[0823] A means of analyzing received data and quantifying participants' behavioral characteristics and performance,

[0824] A means to optimize team composition that takes into account the overall balance of participants, based on quantified behavioral information.

[0825] A means of notifying participants of optimized team formations via information terminals,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, comprising means for storing analyzed behavioral information on a recording medium and generating specific advice for skill improvement for participants based on said record.

[0829] (Claim 3)

[0830] The system according to claim 1, comprising means for providing organizational strategies to strengthen cooperative relationships among participants and for making placement suggestions to facilitate communication.

[0831] "Application Example 1"

[0832] (Claim 1)

[0833] Analyze participant information collected by the imaging device,

[0834] This analysis provides a means to quantify the behavior and performance of participants,

[0835] A means to optimize purchasing trends based on quantified participant behavioral information,

[0836] A means of notifying customers of optimized purchasing trends,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The analyzed participant behavior information is saved as a record.

[0840] Based on these records, this includes means of providing advice to improve the customer's purchasing experience,

[0841] The system according to claim 1.

[0842] (Claim 3)

[0843] This includes means of providing matching to facilitate purchasing behavior among participants and strengthen cooperative relationships,

[0844] The system according to claim 1.

[0845] "Example 2 of combining an emotion engine"

[0846] (Claim 1)

[0847] Analyze participant information collected by the imaging device,

[0848] This analysis provides a means to quantify the behavior and performance of participants,

[0849] A means of integrating quantified participant behavioral and emotional information to generate a skill score that takes into account the player's psychological state,

[0850] A means to optimize team composition based on the generated skill score,

[0851] A means of notifying participants of optimized team composition and providing advice for skill improvement based on emotional changes,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising means for storing analyzed participant behavioral information and emotional data as records and providing advice for the continuous skill improvement of participants based on these.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising means for providing field matching to facilitate smooth communication among participants and strengthen cooperative relationships.

[0857] "Application example 2 when combining with an emotional engine"

[0858] (Claim 1)

[0859] Analyze participant information collected by the imaging device,

[0860] This analysis provides a means to quantify the behavior and performance of participants,

[0861] A means to optimize team composition based on quantified participant behavioral information,

[0862] A means of notifying participants of the optimized team composition,

[0863] A means of recognizing participants' emotional states in real time and adjusting interactive content based on that emotional information,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The analyzed participant behavior information is saved as a record.

[0867] Based on these records, including means of providing advice to improve participants' skills,

[0868] The system according to claim 1.

[0869] (Claim 3)

[0870] A means of providing field matching to promote communication among participants and strengthen collaborative relationships,

[0871] This includes means of providing personalized content suggestions to each viewer based on emotional data.

[0872] The system according to claim 1. [Explanation of Symbols]

[0873] 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. Analyze participant information collected by the imaging device, This analysis provides a means to quantify the behavior and performance of participants, A means to optimize team composition based on quantified participant behavioral information, A means of notifying participants of the optimized team composition, A system that includes this.

2. The analyzed participant behavior information is saved as a record. Based on these records, including means of providing advice to improve participants' skills, The system according to claim 1.

3. This includes means of providing field matching to facilitate communication among participants and strengthen collaborative relationships. The system according to claim 1.

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