system

Abstract

Provide a system. 【Solution means】 Means for collecting participant information, analyzing based on past data and market trends, and generating an event summary; Means for automatically creating a schedule based on the generated summary; Means for providing customized information based on the attributes of individual participants; Means for analyzing real-time information from devices within the venue to understand the behavior of participants; Means for matching between participants and between participants and event content; Means for providing interaction with participants using an AI dialogue system; Means for providing personalized content recommendations based on the user's viewing history; Means for promoting participation and interaction in the generated virtual event; A system including the above.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional event planning and operation, there have been problems such as the difficulty of providing individual support according to the diverse needs of participants, efficiently assembling a schedule within limited resources, grasping and immediately responding to real-time participant behavior. Also, effective matchmaking between participants and smooth response to various inquiries have not been fully implemented.

Means for Solving the Problems

[0005] This invention provides a system that analyzes participant data, automatically generates new event concepts, and provides personalized information to individual participants. Furthermore, it analyzes participant movement using sensor technology and facial recognition technology that enable real-time analysis of participant behavior, and identifies congestion levels. It also solves the aforementioned problems by providing means to support interaction with participants using an AI chatbot and to achieve effective matching between participants and between participants and content.

[0006] "Participants" refer to people who attend an event and experience its programs and sessions.

[0007] "Data collection" refers to the process of gathering information related to an event, such as participant information, activity history, and feedback.

[0008] "Event concept" refers to the theme, ideas, and overall direction of the program offered at a particular event.

[0009] "Automatic schedule creation" refers to the process of automatically assembling event programs and sessions with the optimal time allocation based on collected data.

[0010] "Personalized information" refers to event information and suggestions that are customized according to the interests and needs of individual participants.

[0011] A "sensor" refers to a device used to detect participants' movements and the conditions within the venue in real time.

[0012] "Facial recognition technology" refers to the technology that identifies and recognizes human faces through digital images and videos.

[0013] "Analysis of participant behavior" refers to analyzing participants' trends and behavioral patterns based on data collected using sensors and facial recognition technology.

[0014] An "AI chatbot" refers to a program that utilizes artificial intelligence technology to automate interactions with participants.

[0015] "Matching" refers to the process of optimizing the relationships between participants and between participants and event content, and proposing appropriate encounters and information.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides a system that automatically generates event concepts based on event participant data, creates schedules based on those concepts, and provides personalized information to individual participants. The program processing of this system is described below in natural language, along with specific examples.

[0038] First, the server collects registration information from event participants and past event data, and analyzes trends and participant feedback. Based on this analysis, it proposes an event concept, for example, one related to "next-generation technology." This determines the direction of the event and the content of each session.

[0039] Next, the server automatically creates the event date and a detailed session schedule based on the generated concept, taking into account resources (venue availability and speaker schedules). This enables efficient resource utilization and program planning that minimizes participant travel time.

[0040] The device notifies participants of customized schedules and session recommendations based on their individual profiles. For example, a participant interested in AI technology will be notified of details and times for relevant technical sessions. This allows participants to efficiently experience content that matches their interests.

[0041] Furthermore, the server uses sensors and facial recognition technology installed throughout the venue to analyze participants' movements in real time. Based on the data obtained, it can identify popular booths and crowded areas, and suggest optimal routes and destinations to participants.

[0042] Furthermore, the terminals enable interactive dialogue with participants through an AI chatbot to respond to their inquiries. For example, if a participant asks, "Which session is the most popular?", the chatbot can provide accurate information based on real-time data.

[0043] In this way, the server and terminals work together to provide an optimized event experience for each participant. This system makes it possible to achieve both efficient event management and increased participant satisfaction.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects historical event data and trend information. This includes information such as the number of event attendees, participant feedback, and interest in sessions.

[0047] Step 2:

[0048] The server analyzes the collected data. Machine learning algorithms are used to identify patterns from past successful events and automatically generate new event concepts.

[0049] Step 3:

[0050] The server creates a schedule based on the generated event concept. It efficiently arranges the program, taking into account venue availability, speaker schedules, and other factors.

[0051] Step 4:

[0052] The device accesses participant profile information. Based on registration information and past participation history, it identifies sessions and booths relevant to the participant's interests and needs.

[0053] Step 5:

[0054] The device notifies participants of a customized schedule. Programs reflecting participants' interests are delivered via push notifications and email to support efficient participation.

[0055] Step 6:

[0056] The server monitors participants' movements in real time through sensors and facial recognition cameras within the venue. This allows for the identification of crowded areas and analysis of popular booths.

[0057] Step 7:

[0058] The server matches participants with other participants and relevant content based on their interests, facilitating networking among participants with shared interests.

[0059] Step 8:

[0060] The terminal utilizes an AI chatbot to respond to participant inquiries. It provides participants with the information they need in real time, ensuring a smooth event experience.

[0061] By combining these steps, an optimized event experience is provided for participants, and the efficiency of event management is improved.

[0062] (Example 1)

[0063] 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."

[0064] Traditional event management has faced challenges due to the inefficiency of manually adjusting information to suit each participant's interests and schedule. Furthermore, the inability to optimize participant flow in real time, making it difficult to alleviate congestion, and limited immediate access to the latest information were also problematic.

[0065] 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.

[0066] In this invention, the server includes means for collecting participant data and analyzing it based on data and trend information related to past gatherings to generate a gathering concept; means for automatically creating a gathering schedule based on the generated concept; and means for providing individually tailored information based on information about each participant. This enables efficient management of the entire event and the provision of information optimized for each individual participant.

[0067] "Participant data" refers to information about individuals participating in the event, including their name, contact information, areas of interest, and past participation history.

[0068] "Data on past gatherings" refers to information about events held previously, including elements such as the number of participants, program content, and feedback.

[0069] "Trend information" refers to information based on the latest technological and social trends related to events, and is useful in determining the theme and content of future events.

[0070] The "concept of a gathering" refers to the central theme or concept of an event, derived from the interests and trend information of the participants.

[0071] The "meeting schedule" refers to a plan that includes the date and time of the event, the program structure, and the time schedule for each session.

[0072] "Individually tailored information based on each participant's information" refers to customized content provided to suit each participant's preferences, interests, and schedule.

[0073] A "detector" is a device installed within an event venue to track and analyze the location and movement of participants in real time.

[0074] "Individual recognition technology" refers to technology for identifying participants, and includes technology that identifies individuals using facial recognition or other recognition means.

[0075] An "intelligent program" is software that performs natural language processing and has the ability to provide information and answer questions by interacting with event participants.

[0076] "Means for coordinating spatial arrangement and speaker schedules based on the theme of the generated gathering" refers to the process of efficiently determining the venue layout and optimizing speaker participation times based on the event theme.

[0077] "Means for optimizing participants' movement routes and areas of interest" refers to the process of efficiently guiding participants within a venue and providing recommended behavioral patterns tailored to their individual interests.

[0078] This invention is a system that collects event participant data and provides individually tailored information to deliver an optimal experience to each participant.

[0079] First, the server collects and stores participant data using a database management system. Specifically, MySQL® is used as the database, and libraries such as Python's pandas and scikit-learn are used for data analysis. The server analyzes past event data and trend information and inputs prompts into the generative AI model. An example of such a prompt might be, "Generate event themes based on recent technology trends." Based on the output of this AI, a new concept of gathering is proposed.

[0080] Next, the server automatically creates event dates using the Google® Calendar API based on the generated group concept. The server utilizes linear programming to create the most efficient schedule when coordinating venue layouts and speaker schedules. This ensures that participants can attend the event without wasting time.

[0081] The terminal notifies participants of schedule information sent from the server. Based on the participant's profile information, the terminal recommends the most suitable sessions and exhibits for each participant. For example, it can send specific notifications via a mobile app, such as, "The AI ​​technology session recommended for you starts at 2 PM."

[0082] Furthermore, the server uses IoT sensors and individual recognition technology installed within the venue to analyze participants' movements in real time. For example, it can use Azure's facial recognition API to identify popular areas and congested areas, and based on this, suggest the optimal route for movement.

[0083] Furthermore, the device implements an AI chatbot as an intelligent program, responding to participants' questions in real time. When a user asks, "What are the key points of this session?", it can provide detailed information based on the latest data via a generative AI model.

[0084] In this way, by effectively coordinating the server and terminals, it becomes possible to create an event experience optimized for each individual participant, thereby increasing satisfaction for both participants and the event.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server collects data registered by participants and stores it in a database. It receives participants' personal information, past participation history, and areas of interest as input. The server stores this data in a database system such as MySQL. The output is data organized as individual participant profile information.

[0088] Step 2:

[0089] The server analyzes past data and trend information. It uses historical event data and trend information for input to a generative AI model. The server performs data analysis using Python's pandas and scikit-learn libraries. The output is a prompt message, "Generate event themes based on recent technology trends," to be sent to the generative AI model.

[0090] Step 3:

[0091] The server generates event themes using a generative AI model. It sends prompt text to the AI ​​as input. The server receives the event concepts output by the generative AI model and determines a theme, such as "next-generation technology." The output is the selected event theme.

[0092] Step 4:

[0093] The server automatically creates a schedule based on the event's theme. It takes the event theme, venue layout, and speaker schedules as input from the Google Calendar API. The server uses an optimization algorithm to calculate an efficient schedule and outputs a timetable.

[0094] Step 5:

[0095] The device notifies participants of the schedule information. As input, it receives personalized schedule information for each participant obtained from the server. The device uses a mobile app to send push notifications to participants, outputting a message such as, "The recommended AI technology session starts at 2 PM."

[0096] Step 6:

[0097] The server analyzes participant movement patterns using detectors and individual recognition technology within the venue. It collects real-time data from IoT sensors and facial recognition cameras as input. The server analyzes this data to identify popular areas and congested zones within the venue. The output is a suggested route for participants, incorporating this information.

[0098] Step 7:

[0099] The terminal interacts with participants through an intelligent program. It receives questions from participants as input. Based on information analyzed by an AI chatbot, the terminal provides a detailed answer to the question, "What are the key points of this session?" The output is the immediate provision of the information the participant is seeking.

[0100] (Application Example 1)

[0101] 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."

[0102] Providing an event experience tailored to the individual interests of participants and users in large-scale events and content distribution services is challenging. Furthermore, there is a lack of real-time participant behavior tracking and effective information provision based on that feedback. Additionally, there is a need for methods to improve participant satisfaction by suggesting personalized content based on viewing history and other factors.

[0103] 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.

[0104] In this invention, the server includes means for collecting participant information, analyzing it based on past data and market trends to generate an event overview, automatically creating a schedule based on the generated overview, and providing customized information based on the attributes of individual participants. This makes it possible to provide a personalized event experience tailored to the interests of each participant.

[0105] "Participants" refer to individual people who participate in events or content distribution services with a specific interest or purpose.

[0106] "Collecting information" refers to the act of gathering and organizing data about participants and past trend information.

[0107] "Event Overview" refers to a general plan that outlines the concept and structure of future events, based on the analyzed data.

[0108] "Automatically creating a schedule" refers to the process of mechanically organizing a schedule based on the elements of the generated event overview.

[0109] "Customized information" refers to data and content optimized for the specific interests and needs of individual participants.

[0110] "Personalized content" refers to media and information that are individually suggested based on a user's past viewing history and attribute information.

[0111] "Real-time information" refers to dynamic data that is acquired instantly without any time delay.

[0112] "Analyzing the route" refers to a method of examining participants' movement and location information in detail to identify patterns of movement.

[0113] A "popular area" refers to a specific location that attracts a large number of participants and shows a high level of interest.

[0114] "Personal identification technology" refers to algorithms and devices used to identify individuals.

[0115] The server first collects participant information and performs analysis based on past data and market trends. This stage utilizes programming languages ​​such as Python and data analysis software. Based on the analysis results, an event outline is generated, and then the schedule is automatically created. Resource management tools and scheduling software are used for schedule creation.

[0116] Subsequently, the server uses AI technology to provide customized information based on each participant's profile information. Machine learning models and natural language processing techniques are used to suggest personalized content tailored to individual interests.

[0117] Real-time data analysis utilizes dynamic information acquired from devices within the location. Personal identification technologies, such as facial recognition algorithms and sensors, are used to identify participants' routes and popular areas. This enables the design of efficient and comfortable movement patterns.

[0118] As a concrete example, user A is presented with a "SF movie festival" event that matches their viewing history using this system. Furthermore, the AI ​​dialogue system provides session information tailored to the participant's interests, facilitating interaction.

[0119] An example of a prompt to input into the generation AI model is: "Please provide details about the feature that automatically generates new content events in genres the user is interested in and notifies them with a personalized schedule."

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The server uses participant information as input to retrieve each participant's past participation history and market trend information from the database. This gathers the necessary initial data for the event. This data is then used in subsequent analysis. The analysis program identifies different trends and generates an overview of the new event.

[0123] Step 2:

[0124] The server receives the event summary obtained in Step 1 as input and automatically creates a schedule using scheduling software. In this step, resource allocation and timetable adjustments are made based on the event concept, and the optimal event schedule is output.

[0125] Step 3:

[0126] The server collects participant profile information as input and generates personalized content based on interests using an AI model. This results in customized information being output for each user, with relevant events and media being recommended.

[0127] Step 4:

[0128] To achieve real-time analysis, the server acquires input data from devices within the location and uses facial recognition technology to analyze participants' movement patterns and dwell time. Based on this analysis data, information indicating popular areas and efficient traffic flow design is output.

[0129] Step 5:

[0130] The device receives information from the server and notifies the user of personalized event schedules and content relevant to participants. This notification function works to deliver information and sessions that the user is most likely to be interested in at the optimal time.

[0131] Step 6:

[0132] Based on the notifications they receive, users can participate in events and actively interact with them. Through an AI dialogue system, user questions are answered instantly, and information is provided in line with the progress of the event.

[0133] 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.

[0134] This invention provides a system that automatically generates event concepts based on participant data, creates a schedule based on those concepts, recognizes participants' emotions using an emotion engine, and provides personalized information. The program processing of this system is explained below in natural language, with specific examples.

[0135] First, the server collects participant information for the event and analyzes past event data and the latest trend information. This generates new event concepts, such as "a conference on next-generation technologies." Based on these concepts, the server automatically organizes the detailed program for the event, taking into account available resources and schedules.

[0136] Next, devices within the event venue use sensors and cameras to monitor participants' movements in real time, and the collected data is analyzed on a server. This allows the emotion engine to identify the participants' emotional states, such as whether they are happy or have lost interest.

[0137] For example, if many participants in a session begin to lose interest and leave, the server can detect this and adjust the topic of the next session to match the participants' interests. This dynamic content adjustment allows for flexible responses to changing participant needs.

[0138] Furthermore, the terminals use an AI chatbot to respond to participants' real-time inquiries in an emotionally responsive manner. For example, if a participant is unsure about which booth to choose, and the emotion engine detects that the participant is not interested, the chatbot will suggest the content in a positive and engaging way.

[0139] Furthermore, the server uses a matchmaking algorithm to facilitate networking based on shared interests among participants. The emotion engine analyzes the emotional responses of participants' conversations to support compatible pairings.

[0140] By incorporating participants' real-time emotional changes in this way, it becomes possible to create more personalized event management and improve participant satisfaction.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The server collects registration information from event participants. This includes participants' names, topics of interest, and past event participation history.

[0144] Step 2:

[0145] The server analyzes past event data and the latest trend information to generate new event concepts. This process uses data mining techniques and machine learning algorithms to identify promising themes and content.

[0146] Step 3:

[0147] Based on the generated concept, the server automatically creates an event schedule, taking into account venue availability and speaker schedules. The schedule includes dates, times, and session details.

[0148] Step 4:

[0149] The device uses sensors and cameras within the venue to monitor participants' movements and emotional states in real time. It collects data such as participants' movement, time spent in the venue, and facial expressions.

[0150] Step 5:

[0151] The server analyzes real-time data it collects and uses an emotion engine to recognize the participants' emotions. For example, it can determine whether they are enjoying themselves or have lost interest.

[0152] Step 6:

[0153] The device provides personalized information based on the participant's emotional state. This information includes recommendations for sessions that might interest the participant and suggestions for travel routes.

[0154] Step 7:

[0155] The server uses an AI chatbot to provide emotion-based responses to participant inquiries. It also offers suggestions to make the content more engaging for participants who show little interest.

[0156] Step 8:

[0157] The server uses sentiment data to effectively match participants. It combines participants with similar sentiments and interests to facilitate networking.

[0158] This enables dynamic event management that takes emotions into account, thereby improving the participants' experience.

[0159] (Example 2)

[0160] 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".

[0161] In current event management, there is a challenge in understanding the diverse needs and interests of participants in real time and adjusting content accordingly. Furthermore, effectively utilizing changes in participants' emotions and behavior to provide personalized experiences is not easy. Therefore, it is necessary to maximize participant satisfaction while simultaneously improving operational efficiency.

[0162] 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.

[0163] In this invention, the server includes means for collecting participant information, analyzing past and trend information to generate event themes, automatically creating activity schedules based on the generated themes, and providing personalized information based on individual participant information. This enables flexible program adjustments in response to changes in participants' interests and emotions, and the provision of personalized, high-quality experiences.

[0164] "Participant information" refers to data related to individual users participating in the event, including basic information, past participation history, and trends.

[0165] "Past information" refers to a collection of data from previously held events and related information, which serves as material for analyzing trends and patterns.

[0166] "Trend information" refers to data on new social or industry trends and popular themes that can be useful in developing event concepts.

[0167] An "event theme" refers to the main idea or concept of a particular event, which is intended to attract participants' interest and guide the content of the event.

[0168] "Activity schedule" refers to the specific program and session timetable for the event, and the detailed, systematically organized progress of the event.

[0169] "Personalized information" refers to data and suggestions customized for each participant, addressing their individual interests and preferences.

[0170] A "detector" is a device installed within an event venue that monitors and collects information about participants' movements and actions in real time.

[0171] An "AI dialogue system" is a system that uses natural language processing technology to communicate interactively with participants and provide information and support.

[0172] "Facial expression analysis function" is a technology that analyzes the facial expressions of participants, evaluates their emotional state, and is used to adjust the content of events.

[0173] This invention describes embodiments for carrying it out. The system is configured to automate and optimize the collection and analysis of participant information, the execution of events, and individualized responses to participants.

[0174] The server first collects participant information using a database management system (e.g., Relational Database Management System). The data collected includes basic information about each participant, their past participation history, and survey results. This data is then analyzed using data analysis tools (e.g., Apache® Hadoop or Spark) to provide foundational information for understanding participants' interests and tendencies.

[0175] Furthermore, the server analyzes historical and trending information. Trending information is obtained from internet data streams and industry reports. Based on this analysis, the server generates an event concept, for example, themed "AI and Ethics." This theme is expected to attract the interest of participants.

[0176] Based on the generated event theme, the server automatically organizes the activity schedule using a scheduling algorithm. This algorithm takes into account available resources (e.g., time slots, instructors, venue size) to provide an efficient and engaging timetable. Optimized scripts are used, leveraging a programming language (e.g., Python).

[0177] Multiple terminals will be installed throughout the venue, utilizing sensors and cameras. These devices will monitor participants' movements in real time and perform movement pattern analysis. This monitoring data will be processed by image analysis software (e.g., OpenCV) to provide information for inferring participants' interests and preferences.

[0178] In interacting with participants, the terminal communicates with them through an AI dialogue system. Using natural language processing technology, it analyzes participants' questions and emotional expressions, and provides appropriate responses and suggestions based on that analysis. For example, if a participant is unsure whether to visit a particular booth, the AI ​​dialogue system might suggest, "Why not take this opportunity to visit?" to pique their interest. This system dynamically integrates with each participant's profile information to provide more relevant information.

[0179] As a concrete example, the prompt text when using a generative AI model to devise a theme for a new event would be as follows:

[0180] Prompt message:

[0181] "Based on past technology conference data and the latest technology trends, please propose five interesting session titles and content ideas for a conference focused on next-generation technologies."

[0182] This configuration allows the system to provide a flexible and personalized event experience that reflects participants' real-time situations and emotions.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] The server imports participant information into a database management system. This requires basic participant information, participation history, and survey results as input. The server organizes this data and generates participant profiles using data analysis tools. The output is profile data showing each participant's interests and tendencies.

[0186] Step 2:

[0187] The server analyzes historical and trending information. This process uses data from past events and current events information from the internet as input. Utilizing data analysis tools, the server creates event themes using a generative AI model. Specifically, it suggests themes such as "next-generation technologies" that reflect current trends. The output is the proposed event theme.

[0188] Step 3:

[0189] The server automatically creates an activity schedule based on the generated event theme. The required inputs are theme information and available resources (time slots, instructors, venue information). A scheduling algorithm optimizes resource allocation and generates a detailed timetable. The output is a completed event schedule.

[0190] Step 4:

[0191] The terminal uses sensors and cameras placed throughout the venue to monitor participants' movements in real time. Input data from the sensors includes participants' movements and location information. The terminal uses image analysis software to generate movement data. The output is trend data that reflects participants' interests and preferences.

[0192] Step 5:

[0193] The server receives real-time behavioral data sent from terminals and analyzes participants' emotions. The input data includes information based on participants' movements. An emotion analysis engine is used to identify participants' emotional states. The output is participant emotion evaluation data.

[0194] Step 6:

[0195] The terminal interacts with participants via an AI dialogue system. Input requires natural language questions and requests from participants, and sentiment evaluation data is also utilized. The terminal uses natural language processing technology to generate appropriate responses and provides them to participants in real time. The output is a personalized response provided to the participant.

[0196] Step 7:

[0197] The server facilitates networking by matching participants based on their shared interests. The input consists of participant profile data and sentiment rating data. A matchmaking algorithm identifies common interests among participants and generates combinations that support networking. The output is information on compatible participant pairings.

[0198] (Application Example 2)

[0199] 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 device 14 will be referred to as the "terminal."

[0200] In today's retail industry, there is a demand for providing appropriate information that meets the diverse needs and interests of consumers. However, traditional offline stores find it difficult to instantly optimize the individual customer experience. As a result, there are limitations to improving customer satisfaction and stimulating purchasing intent. To solve these problems, it is necessary to provide personalized purchase suggestions based on real-time customer behavior and sentiment analysis.

[0201] 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.

[0202] In this invention, the server includes means for collecting participant data, analyzing it based on past activity information and trend information to generate event themes, automatically creating activity progress plans based on the generated themes, and providing personalized information based on information of individual participants. This makes it possible to provide product information that is tailored to the customer's interests and emotions in real time.

[0203] "Participant data" refers to information including attribute information, past behavioral history, interests, and preferences of each individual.

[0204] "Activity information" refers to information about events, activities, and store layouts at specific locations and times.

[0205] "Trend information" refers to information based on recent trends and public interest, and is a factor that influences consumers' purchasing decisions.

[0206] An "event theme" is a concept or idea designed as the core of an activity or event being held.

[0207] A "procedure plan" is a plan that defines the order and procedures for running a particular activity or event.

[0208] "Personalized information" refers to customized content and suggestions tailored to an individual's profile.

[0209] A "detection device" is a device that senses the movements and facial expressions of participants in real time and collects that data.

[0210] "Real-time information" refers to data or feedback that immediately reflects ongoing events or situations.

[0211] "Analyzing movement patterns" involves analyzing the routes and flow of participants as they move within the facility and understanding those patterns.

[0212] "Facial recognition technology" is a technology used to identify and authenticate individuals based on their individual faces.

[0213] This invention is a system for analyzing customer behavior and emotions in retail stores and providing personalized information. Specifically, it is implemented as follows:

[0214] The server first acquires real-time information from cameras and sensors installed within the store to collect participant data. These devices include Azure Kinect and motion sensors on a Raspberry Pi. The server analyzes the collected data and generates event themes and schedules based on participants' behavior and emotions. For analysis, machine learning libraries such as TENSORFLOW® and PyTorch are used to identify participants' interests.

[0215] The device provides personalized product information on a smartphone application based on analyzed personal data. This allows users to receive appropriate products and recommendations based on their behavior within the store. For example, if a customer spends a long time in the wine section, the application will immediately notify them of relevant wine promotions.

[0216] Furthermore, the generative AI model uses "Identify the product categories the customer is interested in and generate a list of recommended products based on their sentiment" as an example of a prompt, enhancing the personalized user experience. In this way, the server and terminal work together to provide information that meets individual needs in real time.

[0217] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0218] Step 1:

[0219] The server collects environmental data in real time from Azure Kinect and Raspberry Pi-based motion sensors installed within the store. Input is raw data acquired from camera images and motion sensors, and output is a recorded file of that data. This allows for the accumulation of information regarding customer location and movement.

[0220] Step 2:

[0221] The server analyzes the collected data using TensorFlow or PyTorch. The input is the environmental data collected in step 1, and the output is the analysis results showing the customer's current interest categories and emotional state. The data is processed by machine learning algorithms to identify customer behavior patterns.

[0222] Step 3:

[0223] The terminal provides personalized product information as a push notification to the user's smartphone based on the analysis results received from the server. The input is the analysis results from step 2, and the output is the notification message on the smartphone. This operation allows the user to receive real-time suggestions for products that match their interests within the store.

[0224] Step 4:

[0225] Users receive notifications from their devices and check out interesting product information. Input is push notifications from the device, and output is the user's reactions and feedback. This information is then sent to the server and used for future analysis.

[0226] Step 5:

[0227] The server utilizes a generative AI model based on user response data to generate more refined product suggestions based on specified prompts such as, "Identify the product categories the customer is interested in and generate a list of recommended products based on their sentiment." The input is user feedback data, and the output is new personalized suggestions. The machine learning model is continuously improved throughout this process.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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".

[0244] This invention provides a system that automatically generates event concepts based on event participant data, creates schedules based on those concepts, and provides personalized information to individual participants. The program processing of this system is described below in natural language, along with specific examples.

[0245] First, the server collects registration information from event participants and past event data, and analyzes trends and participant feedback. Based on this analysis, it proposes an event concept, for example, one related to "next-generation technology." This determines the direction of the event and the content of each session.

[0246] Next, the server automatically creates the event date and a detailed session schedule based on the generated concept, taking into account resources (venue availability and speaker schedules). This enables efficient resource utilization and program planning that minimizes participant travel time.

[0247] The device notifies participants of customized schedules and session recommendations based on their individual profiles. For example, a participant interested in AI technology will be notified of details and times for relevant technical sessions. This allows participants to efficiently experience content that matches their interests.

[0248] Furthermore, the server uses sensors and facial recognition technology installed throughout the venue to analyze participants' movements in real time. Based on the data obtained, it can identify popular booths and crowded areas, and suggest optimal routes and destinations to participants.

[0249] Furthermore, the terminals enable interactive dialogue with participants through an AI chatbot to respond to their inquiries. For example, if a participant asks, "Which session is the most popular?", the chatbot can provide accurate information based on real-time data.

[0250] In this way, the server and terminals work together to provide an optimized event experience for each participant. This system makes it possible to achieve both efficient event management and increased participant satisfaction.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server collects historical event data and trend information. This includes information such as the number of event attendees, participant feedback, and interest in sessions.

[0254] Step 2:

[0255] The server analyzes the collected data. Machine learning algorithms are used to identify patterns from past successful events and automatically generate new event concepts.

[0256] Step 3:

[0257] The server creates a schedule based on the generated event concept. It efficiently arranges the program, taking into account venue availability, speaker schedules, and other factors.

[0258] Step 4:

[0259] The device accesses participant profile information. Based on registration information and past participation history, it identifies sessions and booths relevant to the participant's interests and needs.

[0260] Step 5:

[0261] The device notifies participants of a customized schedule. Programs reflecting participants' interests are delivered via push notifications and email to support efficient participation.

[0262] Step 6:

[0263] The server monitors participants' movements in real time through sensors and facial recognition cameras within the venue. This allows for the identification of crowded areas and analysis of popular booths.

[0264] Step 7:

[0265] The server matches participants with other participants and relevant content based on their interests, facilitating networking among participants with shared interests.

[0266] Step 8:

[0267] The terminal utilizes an AI chatbot to respond to participant inquiries. It provides participants with the information they need in real time, ensuring a smooth event experience.

[0268] By combining these steps, an optimized event experience is provided for participants, and the efficiency of event management is improved.

[0269] (Example 1)

[0270] 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."

[0271] Traditional event management has faced challenges due to the inefficiency of manually adjusting information to suit each participant's interests and schedule. Furthermore, the inability to optimize participant flow in real time, making it difficult to alleviate congestion, and limited immediate access to the latest information were also problematic.

[0272] 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.

[0273] In this invention, the server includes means for collecting participant data and analyzing it based on data and trend information related to past gatherings to generate a gathering concept; means for automatically creating a gathering schedule based on the generated concept; and means for providing individually tailored information based on information about each participant. This enables efficient management of the entire event and the provision of information optimized for each individual participant.

[0274] "Participant data" refers to information about individuals participating in the event, including their name, contact information, areas of interest, and past participation history.

[0275] "Data on past gatherings" refers to information about events held previously, including elements such as the number of participants, program content, and feedback.

[0276] "Trend information" refers to information based on the latest technological and social trends related to events, and is useful in determining the theme and content of future events.

[0277] The "concept of a gathering" refers to the central theme or concept of an event, derived from the interests and trend information of the participants.

[0278] The "meeting schedule" refers to a plan that includes the date and time of the event, the program structure, and the time schedule for each session.

[0279] "Individually tailored information based on each participant's information" refers to customized content provided to suit each participant's preferences, interests, and schedule.

[0280] A "detector" is a device installed within an event venue to track and analyze the location and movement of participants in real time.

[0281] "Individual recognition technology" refers to technology for identifying participants, and includes technology that identifies individuals using facial recognition or other recognition means.

[0282] An "intelligent program" is software that performs natural language processing and has the ability to provide information and answer questions by interacting with event participants.

[0283] "Means for adjusting the arrangement in space and the speaker's schedule based on the theme of the generated gathering" refers to the process of efficiently determining the venue layout based on the event theme and optimizing the speaker's participation time.

[0284] "Means for optimizing the movement paths of participants and the gathering areas of interest" refers to the process of efficiently guiding participants within the venue and providing recommended action patterns according to individual interests.

[0285] This invention is a system that collects participant data for an event and provides individually tailored information to offer an optimal experience for each participant.

[0286] First, the server uses a database management system to collect and store participant data. As a specific example, MySQL is used as the database, and libraries such as Python's pandas and scikit-learn are used for data analysis. The server analyzes past event data and trend information and inputs prompts into the generative AI model. An example of such a prompt sentence could be "Please generate an event theme based on recent technology trends." Based on the output of this AI, a new gathering concept is proposed.

[0287] Next, based on the generated gathering concept, the server automatically creates the event schedule using the Google Calendar API. When adjusting the venue layout and the speaker's schedule, the server utilizes linear programming to formulate the most efficient schedule. This enables participants to attend the event without waste.

[0288] The terminal notifies the participants of the schedule information sent from the server. Based on the participant's profile information, the terminal recommends the most suitable sessions and exhibitions for each participant. For example, through a mobile app, specific notifications such as "The AI technology session recommended for you starts at 2 pm" can be made.

[0289] Furthermore, the server uses IoT sensors and individual recognition technology installed within the venue to analyze participants' movements in real time. For example, it can use Azure's facial recognition API to identify popular areas and congested areas, and based on this, suggest the optimal route for movement.

[0290] Furthermore, the device implements an AI chatbot as an intelligent program, responding to participants' questions in real time. When a user asks, "What are the key points of this session?", it can provide detailed information based on the latest data via a generative AI model.

[0291] In this way, by effectively coordinating the server and terminals, it becomes possible to create an event experience optimized for each individual participant, thereby increasing satisfaction for both participants and the event.

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The server collects data registered by participants and stores it in a database. It receives participants' personal information, past participation history, and areas of interest as input. The server stores this data in a database system such as MySQL. The output is data organized as individual participant profile information.

[0295] Step 2:

[0296] The server analyzes past data and trend information. It uses historical event data and trend information for input to a generative AI model. The server performs data analysis using Python's pandas and scikit-learn libraries. The output is a prompt message, "Generate event themes based on recent technology trends," to be sent to the generative AI model.

[0297] Step 3:

[0298] The server generates event themes using a generative AI model. It sends prompt text to the AI ​​as input. The server receives the event concepts output by the generative AI model and determines a theme, such as "next-generation technology." The output is the selected event theme.

[0299] Step 4:

[0300] The server automatically creates a schedule based on the event's theme. It takes the event theme, venue layout, and speaker schedules as input from the Google Calendar API. The server uses an optimization algorithm to calculate an efficient schedule and outputs a timetable.

[0301] Step 5:

[0302] The device notifies participants of the schedule information. As input, it receives personalized schedule information for each participant obtained from the server. The device uses a mobile app to send push notifications to participants, outputting a message such as, "The recommended AI technology session starts at 2 PM."

[0303] Step 6:

[0304] The server analyzes participant movement patterns using detectors and individual recognition technology within the venue. It collects real-time data from IoT sensors and facial recognition cameras as input. The server analyzes this data to identify popular areas and congested zones within the venue. The output is a suggested route for participants, incorporating this information.

[0305] Step 7:

[0306] The terminal interacts with participants via an intelligent program. As input, it receives questions from the participants. Based on the information analyzed by the AI chatbot, the terminal provides a detailed answer to the question "What are the key points of this session?" The output is the immediate provision of the information required by the participants.

[0307] (Application Example 1)

[0308] 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".

[0309] In large-scale events and content delivery services, it is difficult to provide an event experience according to the interests of each participant or user. Also, there is a lack of real-time understanding of participant behavior and effective information provision based on such feedback. Furthermore, there is a need for a method to propose personalized content based on viewing history, etc., to improve participant satisfaction.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0311] In this invention, the server includes means for collecting participant information, analyzing it based on past data and market trends to generate an event summary, automatically creating a schedule based on the generated summary, and providing customized information based on the attributes of individual participants. This enables the provision of a personalized event experience according to the interests of each participant.

[0312] "Participant" refers to an individual person who participates in an event or content delivery service with interests and purposes. <Q

[0313] "Collecting information" refers to the act of collecting and organizing data related to participants and past trend information.

[0314] "Event Overview" refers to a general plan that outlines the concept and structure of future events, based on the analyzed data.

[0315] "Automatically creating a schedule" refers to the process of mechanically organizing a schedule based on the elements of the generated event overview.

[0316] "Customized information" refers to data and content optimized for the specific interests and needs of individual participants.

[0317] "Personalized content" refers to media and information that are individually suggested based on a user's past viewing history and attribute information.

[0318] "Real-time information" refers to dynamic data that is acquired instantly without any time delay.

[0319] "Analyzing the route" refers to a method of examining participants' movement and stay information in detail to identify patterns of movement.

[0320] A "popular area" refers to a specific location that attracts a large number of participants and shows a high level of interest.

[0321] "Personal identification technology" refers to algorithms and devices used to identify individuals.

[0322] The server first collects participant information and performs analysis based on past data and market trends. This stage utilizes programming languages ​​such as Python and data analysis software. Based on the analysis results, an event outline is generated, and then the schedule is automatically created. Resource management tools and scheduling software are used for schedule creation.

[0323] Subsequently, the server uses AI technology to provide customized information based on each participant's profile information. Machine learning models and natural language processing techniques are used to suggest personalized content tailored to individual interests.

[0324] Real-time data analysis utilizes dynamic information acquired from devices within the location. Personal identification technologies, such as facial recognition algorithms and sensors, are used to identify participants' routes and popular areas. This enables the design of efficient and comfortable movement patterns.

[0325] As a concrete example, user A is presented with a "SF movie festival" event that matches their viewing history using this system. Furthermore, the AI ​​dialogue system provides session information tailored to the participant's interests, facilitating interaction.

[0326] An example of a prompt to input into the generation AI model is: "Please provide details about the feature that automatically generates new content events in genres the user is interested in and notifies them with a personalized schedule."

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The server uses participant information as input to retrieve each participant's past participation history and market trend information from the database. This gathers the necessary initial data for the event. This data is then used in subsequent analysis. The analysis program identifies different trends and generates an overview of the new event.

[0330] Step 2:

[0331] The server receives the event summary obtained in Step 1 as input and automatically creates a schedule using scheduling software. In this step, resource allocation and timetable adjustments are made based on the event concept, and the optimal event schedule is output.

[0332] Step 3:

[0333] The server collects participant profile information as input and generates personalized content based on interests using an AI model. This results in customized information being output for each user, with relevant events and media being recommended.

[0334] Step 4:

[0335] To achieve real-time analysis, the server acquires input data from devices within the location and uses facial recognition technology to analyze participants' movement patterns and dwell time. Based on this analysis data, information indicating popular areas and efficient traffic flow design is output.

[0336] Step 5:

[0337] The device receives information from the server and notifies the user of personalized event schedules and content relevant to participants. This notification function works to deliver information and sessions that the user is most likely to be interested in at the optimal time.

[0338] Step 6:

[0339] Based on the notifications they receive, users can participate in events and actively interact with them. Through an AI dialogue system, user questions are answered instantly, and information is provided in line with the progress of the event.

[0340] 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.

[0341] This invention provides a system that automatically generates event concepts based on participant data, creates a schedule based on those concepts, recognizes participants' emotions using an emotion engine, and provides personalized information. The program processing of this system is explained below in natural language, with specific examples.

[0342] First, the server collects participant information for the event and analyzes past event data and the latest trend information. This generates new event concepts, such as "a conference on next-generation technologies." Based on these concepts, the server automatically organizes the detailed program for the event, taking into account available resources and schedules.

[0343] Next, devices within the event venue use sensors and cameras to monitor participants' movements in real time, and the collected data is analyzed on a server. This allows the emotion engine to identify the participants' emotional states, such as whether they are happy or have lost interest.

[0344] For example, if many participants in a session begin to lose interest and leave, the server can detect this and adjust the topic of the next session to match the participants' interests. This dynamic content adjustment allows for flexible responses to changing participant needs.

[0345] Furthermore, the terminals use an AI chatbot to respond to participants' real-time inquiries in an emotionally responsive manner. For example, if a participant is unsure about which booth to choose, and the emotion engine detects that the participant is not interested, the chatbot will suggest the content in a positive and engaging way.

[0346] Furthermore, the server uses a matchmaking algorithm to facilitate networking based on shared interests among participants. The emotion engine analyzes the emotional responses of participants' conversations to support compatible pairings.

[0347] By incorporating participants' real-time emotional changes in this way, it becomes possible to create more personalized event management and improve participant satisfaction.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The server collects registration information from event participants. This includes participants' names, topics of interest, and past event participation history.

[0351] Step 2:

[0352] The server analyzes past event data and the latest trend information to generate new event concepts. This process uses data mining techniques and machine learning algorithms to identify promising themes and content.

[0353] Step 3:

[0354] Based on the generated concept, the server automatically creates an event schedule, taking into account venue availability and speaker schedules. The schedule includes dates, times, and session details.

[0355] Step 4:

[0356] The device uses sensors and cameras within the venue to monitor participants' movements and emotional states in real time. It collects data such as participants' movement, time spent in the venue, and facial expressions.

[0357] Step 5:

[0358] The server analyzes real-time data it collects and uses an emotion engine to recognize the participants' emotions. For example, it can determine whether they are enjoying themselves or have lost interest.

[0359] Step 6:

[0360] The device provides personalized information based on the participant's emotional state. This information includes recommendations for sessions that might interest the participant and suggestions for travel routes.

[0361] Step 7:

[0362] The server uses an AI chatbot to provide emotion-based responses to participant inquiries. It also offers suggestions to make the content more engaging for participants who show little interest.

[0363] Step 8:

[0364] The server uses sentiment data to effectively match participants. It combines participants with similar sentiments and interests to facilitate networking.

[0365] This enables dynamic event management that takes emotions into account, thereby improving the participants' experience.

[0366] (Example 2)

[0367] 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".

[0368] In current event management, there is a challenge in understanding the diverse needs and interests of participants in real time and adjusting content accordingly. Furthermore, effectively utilizing changes in participants' emotions and behavior to provide personalized experiences is not easy. Therefore, it is necessary to maximize participant satisfaction while simultaneously improving operational efficiency.

[0369] 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.

[0370] In this invention, the server includes means for collecting participant information, analyzing past and trend information to generate event themes, automatically creating activity schedules based on the generated themes, and providing personalized information based on individual participant information. This enables flexible program adjustments in response to changes in participants' interests and emotions, and the provision of personalized, high-quality experiences.

[0371] "Participant information" refers to data related to individual users participating in an event, including basic information, past participation history, and trends.

[0372] "Past information" refers to a collection of data from previously held events and related information, which serves as material for analyzing trends and patterns.

[0373] "Trend information" refers to data on new social or industry trends and popular themes that can be useful in developing event concepts.

[0374] An "event theme" refers to the main idea or concept of a particular event, which is intended to attract participants' interest and guide the content of the event.

[0375] "Activity schedule" refers to the specific program and session timetable for the event, and the detailed, systematically organized progress of the event.

[0376] "Personalized information" refers to data and suggestions customized for each participant, addressing their individual interests and preferences.

[0377] A "detector" is a device installed within an event venue that monitors and collects information about participants' movements and actions in real time.

[0378] An "AI dialogue system" is a system that uses natural language processing technology to communicate interactively with participants and provide information and support.

[0379] "Facial expression analysis function" is a technology that analyzes the facial expressions of participants, evaluates their emotional state, and is used to adjust the content of events.

[0380] This invention describes embodiments for carrying it out. The system is configured to automate and optimize the collection and analysis of participant information, the execution of events, and individualized responses to participants.

[0381] The server first collects participant information using a database management system (e.g., a Relational Database Management System). The data collected includes basic information about each participant, their past participation history, and survey results. This data is then analyzed using data analysis tools (e.g., Apache Hadoop or Spark) to provide foundational information for understanding participants' interests and trends.

[0382] Furthermore, the server analyzes historical and trending information. Trending information is obtained from internet data streams and industry reports. Based on this analysis, the server generates an event concept, for example, themed "AI and Ethics." This theme is expected to attract the interest of participants.

[0383] Based on the generated event theme, the server automatically organizes the activity schedule using a scheduling algorithm. This algorithm takes into account available resources (e.g., time slots, instructors, venue size) to provide an efficient and engaging timetable. Optimized scripts are used, leveraging a programming language (e.g., Python).

[0384] Multiple terminals will be installed throughout the venue, utilizing sensors and cameras. These devices will monitor participants' movements in real time and perform movement pattern analysis. This monitoring data will be processed by image analysis software (e.g., OpenCV) to provide information for inferring participants' interests and preferences.

[0385] In interacting with participants, the terminal communicates with them through an AI dialogue system. Using natural language processing technology, it analyzes participants' questions and emotional expressions, and provides appropriate responses and suggestions based on that analysis. For example, if a participant is unsure whether to visit a particular booth, the AI ​​dialogue system might suggest, "Why not take this opportunity to visit?" to pique their interest. This system dynamically integrates with each participant's profile information to provide more relevant information.

[0386] As a concrete example, the prompt text when using a generative AI model to devise a theme for a new event would be as follows:

[0387] Prompt message:

[0388] "Based on past technology conference data and the latest technology trends, please propose five interesting session titles and content ideas for a conference focused on next-generation technologies."

[0389] This configuration allows the system to provide a flexible and personalized event experience that reflects participants' real-time situations and emotions.

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Step 1:

[0392] The server imports participant information into a database management system. This requires basic participant information, participation history, and survey results as input. The server organizes this data and generates participant profiles using data analysis tools. The output is profile data showing each participant's interests and tendencies.

[0393] Step 2:

[0394] The server analyzes historical and trending information. This process uses data from past events and current events information from the internet as input. Utilizing data analysis tools, the server creates event themes using a generative AI model. Specifically, it suggests themes such as "next-generation technologies" that reflect current trends. The output is the proposed event theme.

[0395] Step 3:

[0396] The server automatically creates an activity schedule based on the generated event theme. The required inputs are theme information and available resources (time slots, instructors, venue information). A scheduling algorithm optimizes resource allocation and generates a detailed timetable. The output is a completed event schedule.

[0397] Step 4:

[0398] The terminal uses sensors and cameras placed throughout the venue to monitor participants' movements in real time. Input data from the sensors includes participants' movements and location information. The terminal uses image analysis software to generate movement data. The output is trend data that reflects participants' interests and preferences.

[0399] Step 5:

[0400] The server receives real-time behavioral data sent from terminals and analyzes participants' emotions. The input data includes information based on participants' movements. An emotion analysis engine is used to identify participants' emotional states. The output is participant emotion evaluation data.

[0401] Step 6:

[0402] The terminal interacts with participants via an AI dialogue system. Input requires natural language questions and requests from participants, and sentiment evaluation data is also utilized. The terminal uses natural language processing technology to generate appropriate responses and provides them to participants in real time. The output is a personalized response provided to the participant.

[0403] Step 7:

[0404] The server facilitates networking by matching participants based on their shared interests. The input consists of participant profile data and sentiment rating data. A matchmaking algorithm identifies common interests among participants and generates combinations that support networking. The output is information on compatible participant pairings.

[0405] (Application Example 2)

[0406] 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."

[0407] In today's retail industry, there is a demand for providing appropriate information that meets the diverse needs and interests of consumers. However, traditional offline stores find it difficult to instantly optimize the individual customer experience. As a result, there are limitations to improving customer satisfaction and stimulating purchasing intent. To solve these problems, it is necessary to provide personalized purchase suggestions based on real-time customer behavior and sentiment analysis.

[0408] 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.

[0409] In this invention, the server includes means for collecting participant data, analyzing it based on past activity information and trend information to generate event themes, automatically creating activity progress plans based on the generated themes, and providing personalized information based on information of individual participants. This makes it possible to provide product information that is tailored to the customer's interests and emotions in real time.

[0410] "Participant data" refers to information including attribute information, past behavioral history, interests, and preferences of each individual.

[0411] "Activity information" refers to information about events, activities, and store layouts at specific locations and times.

[0412] "Trend information" refers to information based on recent trends and public interest, and is a factor that influences consumers' purchasing decisions.

[0413] An "event theme" is a concept or idea designed as the core of an activity or event being held.

[0414] A "procedure plan" is a plan that defines the order and procedures for running a particular activity or event.

[0415] "Personalized information" refers to customized content and suggestions tailored to an individual's profile.

[0416] A "detection device" is a device that senses the movements and facial expressions of participants in real time and collects that data.

[0417] "Real-time information" refers to data or feedback that immediately reflects ongoing events or situations.

[0418] "Analyzing movement patterns" involves analyzing the routes and flow of participants as they move within the facility and understanding those patterns.

[0419] "Facial recognition technology" is a technology used to identify and authenticate individuals based on their individual faces.

[0420] This invention is a system for analyzing customer behavior and emotions in retail stores and providing personalized information. Specifically, it is implemented as follows:

[0421] The server first acquires real-time information from cameras and sensors installed within the store to collect participant data. These devices include Azure Kinect and motion sensors on a Raspberry Pi. The server analyzes the collected data to generate event themes and schedules based on participants' behavior and emotions. Machine learning libraries such as TensorFlow and PyTorch are used in the analysis to identify participants' interests.

[0422] The device provides personalized product information on a smartphone application based on analyzed personal data. This allows users to receive appropriate products and recommendations based on their behavior within the store. For example, if a customer spends a long time in the wine section, the application will immediately notify them of relevant wine promotions.

[0423] Furthermore, the generative AI model uses "Identify the product categories the customer is interested in and generate a list of recommended products based on their sentiment" as an example of a prompt, enhancing the personalized user experience. In this way, the server and terminal work together to provide information that meets individual needs in real time.

[0424] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0425] Step 1:

[0426] The server collects environmental data in real time from Azure Kinect and Raspberry Pi-based motion sensors installed within the store. Input is raw data acquired from camera images and motion sensors, and output is a recorded file of that data. This allows for the accumulation of information regarding customer location and movement.

[0427] Step 2:

[0428] The server analyzes the collected data using TensorFlow or PyTorch. The input is the environmental data collected in step 1, and the output is the analysis results showing the customer's current interest categories and emotional state. The data is processed by machine learning algorithms to identify customer behavior patterns.

[0429] Step 3:

[0430] The terminal provides personalized product information as a push notification to the user's smartphone based on the analysis results received from the server. The input is the analysis results from step 2, and the output is the notification message on the smartphone. This operation allows the user to receive real-time suggestions for products that match their interests within the store.

[0431] Step 4:

[0432] Users receive notifications from their devices and check out interesting product information. Input is push notifications from the device, and output is the user's reactions and feedback. This information is then sent to the server and used for future analysis.

[0433] Step 5:

[0434] The server utilizes a generative AI model based on user response data to generate more refined product suggestions based on specified prompts such as, "Identify the product categories the customer is interested in and generate a list of recommended products based on their sentiment." The input is user feedback data, and the output is new personalized suggestions. The machine learning model is continuously improved throughout this process.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] [Third Embodiment]

[0439] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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".

[0451] This invention provides a system that automatically generates event concepts based on event participant data, creates schedules based on those concepts, and provides personalized information to individual participants. The program processing of this system is described below in natural language, along with specific examples.

[0452] First, the server collects registration information from event participants and past event data, and analyzes trends and participant feedback. Based on this analysis, it proposes an event concept, for example, one related to "next-generation technology." This determines the direction of the event and the content of each session.

[0453] Next, the server automatically creates the event date and a detailed session schedule based on the generated concept, taking into account resources (venue availability and speaker schedules). This enables efficient resource utilization and program planning that minimizes participant travel time.

[0454] The device notifies participants of customized schedules and session recommendations based on their individual profiles. For example, a participant interested in AI technology will be notified of details and times for relevant technical sessions. This allows participants to efficiently experience content that matches their interests.

[0455] Furthermore, the server uses sensors and facial recognition technology installed throughout the venue to analyze participants' movements in real time. Based on the data obtained, it can identify popular booths and crowded areas, and suggest optimal routes and destinations to participants.

[0456] Furthermore, the terminals enable interactive dialogue with participants through an AI chatbot to respond to their inquiries. For example, if a participant asks, "Which session is the most popular?", the chatbot can provide accurate information based on real-time data.

[0457] In this way, the server and terminals work together to provide an optimized event experience for each participant. This system makes it possible to achieve both efficient event management and increased participant satisfaction.

[0458] The following describes the processing flow.

[0459] Step 1:

[0460] The server collects historical event data and trend information. This includes information such as the number of event attendees, participant feedback, and interest in sessions.

[0461] Step 2:

[0462] The server analyzes the collected data. Machine learning algorithms are used to identify patterns from past successful events and automatically generate new event concepts.

[0463] Step 3:

[0464] The server creates a schedule based on the generated event concept. It efficiently arranges the program, taking into account venue availability, speaker schedules, and other factors.

[0465] Step 4:

[0466] The device accesses participant profile information. Based on registration information and past participation history, it identifies sessions and booths relevant to the participant's interests and needs.

[0467] Step 5:

[0468] The device notifies participants of a customized schedule. Programs reflecting participants' interests are delivered via push notifications and email to support efficient participation.

[0469] Step 6:

[0470] The server monitors participants' movements in real time through sensors and facial recognition cameras within the venue. This allows for the identification of crowded areas and analysis of popular booths.

[0471] Step 7:

[0472] The server matches participants with other participants and relevant content based on their interests, facilitating networking among participants with shared interests.

[0473] Step 8:

[0474] The terminal utilizes an AI chatbot to respond to participant inquiries. It provides participants with the information they need in real time, ensuring a smooth event experience.

[0475] By combining these steps, an optimized event experience is provided for participants, and the efficiency of event management is improved.

[0476] (Example 1)

[0477] 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."

[0478] Traditional event management has faced challenges due to the inefficiency of manually adjusting information to suit each participant's interests and schedule. Furthermore, the inability to optimize participant flow in real time, making it difficult to alleviate congestion, and limited immediate access to the latest information were also problematic.

[0479] 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.

[0480] In this invention, the server includes means for collecting participant data and analyzing it based on data and trend information related to past gatherings to generate a gathering concept; means for automatically creating a gathering schedule based on the generated concept; and means for providing individually tailored information based on information about each participant. This enables efficient management of the entire event and the provision of information optimized for each individual participant.

[0481] "Participant data" refers to information about individuals participating in the event, including their name, contact information, areas of interest, and past participation history.

[0482] "Data on past gatherings" refers to information about events held previously, including elements such as the number of participants, program content, and feedback.

[0483] "Trend information" refers to information based on the latest technological and social trends related to events, and is useful in determining the theme and content of future events.

[0484] The "concept of a gathering" refers to the central theme or concept of an event, derived from the interests and trend information of the participants.

[0485] The "meeting schedule" refers to a plan that includes the date and time of the event, the program structure, and the time schedule for each session.

[0486] "Individually tailored information based on each participant's information" refers to customized content provided to suit each participant's preferences, interests, and schedule.

[0487] A "detector" is a device installed within an event venue to track and analyze the location and movement of participants in real time.

[0488] "Individual recognition technology" refers to technology for identifying participants, and includes technology that identifies individuals using facial recognition or other recognition means.

[0489] An "intelligent program" is software that performs natural language processing and has the ability to provide information and answer questions by interacting with event participants.

[0490] "Means for coordinating spatial arrangement and speaker schedules based on the theme of the generated gathering" refers to the process of efficiently determining the venue layout and optimizing speaker participation times based on the event theme.

[0491] "Means for optimizing participants' movement routes and areas of interest" refers to the process of efficiently guiding participants within a venue and providing recommended behavioral patterns tailored to their individual interests.

[0492] This invention is a system that collects event participant data and provides individually tailored information to deliver an optimal experience to each participant.

[0493] First, the server collects and stores participant data using a database management system. Specifically, MySQL is used as the database, and libraries such as Python's pandas and scikit-learn are used for data analysis. The server analyzes past event data and trend information and inputs prompts into the generative AI model. An example of such a prompt is, "Generate event themes based on recent technology trends." Based on the output of this AI, a new concept of gathering is proposed.

[0494] Next, the server uses the Google Calendar API to automatically create event dates based on the generated group concept. The server employs linear programming to create the most efficient schedule when coordinating venue space and speaker schedules, ensuring participants can attend the event without wasting time.

[0495] The terminal notifies participants of schedule information sent from the server. Based on the participant's profile information, the terminal recommends the most suitable sessions and exhibits for each participant. For example, it can send specific notifications via a mobile app, such as, "The AI ​​technology session recommended for you starts at 2 PM."

[0496] Furthermore, the server uses IoT sensors and individual recognition technology installed within the venue to analyze participants' movements in real time. For example, it can use Azure's facial recognition API to identify popular areas and congested areas, and based on this, suggest the optimal route for movement.

[0497] Furthermore, the device implements an AI chatbot as an intelligent program, responding to participants' questions in real time. When a user asks, "What are the key points of this session?", it can provide detailed information based on the latest data via a generative AI model.

[0498] In this way, by effectively coordinating the server and terminals, it becomes possible to create an event experience optimized for each individual participant, thereby increasing satisfaction for both participants and the event.

[0499] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0500] Step 1:

[0501] The server collects data registered by participants and stores it in a database. It receives participants' personal information, past participation history, and areas of interest as input. The server stores this data in a database system such as MySQL. The output is data organized as individual participant profile information.

[0502] Step 2:

[0503] The server analyzes past data and trend information. It uses historical event data and trend information for input to a generative AI model. The server performs data analysis using Python's pandas and scikit-learn libraries. The output is a prompt message, "Generate event themes based on recent technology trends," to be sent to the generative AI model.

[0504] Step 3:

[0505] The server generates event themes using a generative AI model. It sends prompt text to the AI ​​as input. The server receives the event concepts output by the generative AI model and determines a theme, such as "next-generation technology." The output is the selected event theme.

[0506] Step 4:

[0507] The server automatically creates a schedule based on the event's theme. It takes the event theme, venue layout, and speaker schedules as input from the Google Calendar API. The server uses an optimization algorithm to calculate an efficient schedule and outputs a timetable.

[0508] Step 5:

[0509] The device notifies participants of the schedule information. As input, it receives personalized schedule information for each participant obtained from the server. The device uses a mobile app to send push notifications to participants, outputting a message such as, "The recommended AI technology session starts at 2 PM."

[0510] Step 6:

[0511] The server analyzes participant movement patterns using detectors and individual recognition technology within the venue. It collects real-time data from IoT sensors and facial recognition cameras as input. The server analyzes this data to identify popular areas and congested zones within the venue. The output is a suggested route for participants, incorporating this information.

[0512] Step 7:

[0513] The terminal interacts with participants through an intelligent program. It receives questions from participants as input. Based on information analyzed by an AI chatbot, the terminal provides a detailed answer to the question, "What are the key points of this session?" The output is the immediate provision of the information the participant is seeking.

[0514] (Application Example 1)

[0515] 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."

[0516] Providing an event experience tailored to the individual interests of participants and users in large-scale events and content distribution services is challenging. Furthermore, there is a lack of real-time participant behavior tracking and effective information provision based on that feedback. Additionally, there is a need for methods to improve participant satisfaction by suggesting personalized content based on viewing history and other factors.

[0517] 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.

[0518] In this invention, the server includes means for collecting participant information, analyzing it based on past data and market trends to generate an event overview, automatically creating a schedule based on the generated overview, and providing customized information based on the attributes of individual participants. This makes it possible to provide a personalized event experience tailored to the interests of each participant.

[0519] "Participants" refer to individual people who participate in events or content distribution services with a specific interest or purpose.

[0520] "Collecting information" refers to the act of gathering and organizing data about participants and past trend information.

[0521] "Event Overview" refers to a general plan that outlines the concept and structure of future events, based on the analyzed data.

[0522] "Automatically creating a schedule" refers to the process of mechanically organizing a schedule based on the elements of the generated event overview.

[0523] "Customized information" refers to data and content optimized for the specific interests and needs of individual participants.

[0524] "Personalized content" refers to media and information that are individually suggested based on a user's past viewing history and attribute information.

[0525] "Real-time information" refers to dynamic data that is acquired instantly without any time delay.

[0526] "Analyzing the route" refers to a method of examining participants' movement and location information in detail to identify patterns of movement.

[0527] A "popular area" refers to a specific location that attracts a large number of participants and shows a high level of interest.

[0528] "Personal identification technology" refers to algorithms and devices used to identify individuals.

[0529] The server first collects participant information and performs analysis based on past data and market trends. This stage utilizes programming languages ​​such as Python and data analysis software. Based on the analysis results, an event outline is generated, and then the schedule is automatically created. Resource management tools and scheduling software are used for schedule creation.

[0530] Subsequently, the server uses AI technology to provide customized information based on each participant's profile information. Machine learning models and natural language processing techniques are used to suggest personalized content tailored to individual interests.

[0531] Real-time data analysis utilizes dynamic information acquired from devices within the location. Personal identification technologies, such as facial recognition algorithms and sensors, are used to identify participants' routes and popular areas. This enables the design of efficient and comfortable movement patterns.

[0532] As a concrete example, user A is presented with a "SF movie festival" event that matches their viewing history using this system. Furthermore, the AI ​​dialogue system provides session information tailored to the participant's interests, facilitating interaction.

[0533] An example of a prompt to input into the generation AI model is: "Please provide details about the feature that automatically generates new content events in genres the user is interested in and notifies them with a personalized schedule."

[0534] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0535] Step 1:

[0536] The server uses participant information as input to retrieve each participant's past participation history and market trend information from the database. This gathers the necessary initial data for the event. This data is then used in subsequent analysis. The analysis program identifies different trends and generates an overview of the new event.

[0537] Step 2:

[0538] The server receives the event summary obtained in Step 1 as input and automatically creates a schedule using scheduling software. In this step, resource allocation and timetable adjustments are made based on the event concept, and the optimal event schedule is output.

[0539] Step 3:

[0540] The server collects participant profile information as input and generates personalized content based on interests using an AI model. This results in customized information being output for each user, with relevant events and media being recommended.

[0541] Step 4:

[0542] To achieve real-time analysis, the server acquires input data from devices within the location and uses facial recognition technology to analyze participants' movement patterns and dwell time. Based on this analysis data, information indicating popular areas and efficient traffic flow design is output.

[0543] Step 5:

[0544] The device receives information from the server and notifies the user of personalized event schedules and content relevant to participants. This notification function works to deliver information and sessions that the user is most likely to be interested in at the optimal time.

[0545] Step 6:

[0546] Based on the notifications they receive, users can participate in events and actively interact with them. Through an AI dialogue system, user questions are answered instantly, and information is provided in line with the progress of the event.

[0547] 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.

[0548] This invention provides a system that automatically generates event concepts based on participant data, creates a schedule based on those concepts, recognizes participants' emotions using an emotion engine, and provides personalized information. The program processing of this system is explained below in natural language, with specific examples.

[0549] First, the server collects participant information for the event and analyzes past event data and the latest trend information. This generates new event concepts, such as "a conference on next-generation technologies." Based on these concepts, the server automatically organizes the detailed program for the event, taking into account available resources and schedules.

[0550] Next, devices within the event venue use sensors and cameras to monitor participants' movements in real time, and the collected data is analyzed on a server. This allows the emotion engine to identify the participants' emotional states, such as whether they are happy or have lost interest.

[0551] For example, if many participants in a session begin to lose interest and leave, the server can detect this and adjust the topic of the next session to match the participants' interests. This dynamic content adjustment allows for flexible responses to changing participant needs.

[0552] Furthermore, the terminals use an AI chatbot to respond to participants' real-time inquiries in an emotionally responsive manner. For example, if a participant is unsure about which booth to choose, and the emotion engine detects that the participant is not interested, the chatbot will suggest the content in a positive and engaging way.

[0553] Furthermore, the server uses a matchmaking algorithm to facilitate networking based on shared interests among participants. The emotion engine analyzes the emotional responses of participants' conversations to support compatible pairings.

[0554] By incorporating participants' real-time emotional changes in this way, it becomes possible to create more personalized event management and improve participant satisfaction.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] The server collects registration information from event participants. This includes participants' names, topics of interest, and past event participation history.

[0558] Step 2:

[0559] The server analyzes past event data and the latest trend information to generate new event concepts. This process uses data mining techniques and machine learning algorithms to identify promising themes and content.

[0560] Step 3:

[0561] Based on the generated concept, the server automatically creates an event schedule, taking into account venue availability and speaker schedules. The schedule includes dates, times, and session details.

[0562] Step 4:

[0563] The device uses sensors and cameras within the venue to monitor participants' movements and emotional states in real time. It collects data such as participants' movement, time spent in the venue, and facial expressions.

[0564] Step 5:

[0565] The server analyzes real-time data it collects and uses an emotion engine to recognize the participants' emotions. For example, it can determine whether they are enjoying themselves or have lost interest.

[0566] Step 6:

[0567] The device provides personalized information based on the participant's emotional state. This information includes recommendations for sessions that might interest the participant and suggestions for travel routes.

[0568] Step 7:

[0569] The server uses an AI chatbot to provide emotion-based responses to participant inquiries. It also offers suggestions to make the content more engaging for participants who show little interest.

[0570] Step 8:

[0571] The server uses sentiment data to effectively match participants. It combines participants with similar sentiments and interests to facilitate networking.

[0572] This enables dynamic event management that takes emotions into account, thereby improving the participants' experience.

[0573] (Example 2)

[0574] 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."

[0575] In current event management, there is a challenge in understanding the diverse needs and interests of participants in real time and adjusting content accordingly. Furthermore, effectively utilizing changes in participants' emotions and behavior to provide personalized experiences is not easy. Therefore, it is necessary to maximize participant satisfaction while simultaneously improving operational efficiency.

[0576] 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.

[0577] In this invention, the server includes means for collecting participant information, analyzing past and trend information to generate event themes, automatically creating activity schedules based on the generated themes, and providing personalized information based on individual participant information. This enables flexible program adjustments in response to changes in participants' interests and emotions, and the provision of personalized, high-quality experiences.

[0578] "Participant information" refers to data related to individual users participating in the event, including basic information, past participation history, and trends.

[0579] "Past information" refers to a collection of data from previously held events and related information, which serves as material for analyzing trends and patterns.

[0580] "Trend information" refers to data on new social or industry trends and popular themes that can be useful in developing event concepts.

[0581] An "event theme" refers to the main idea or concept of a particular event, which is intended to attract participants' interest and guide the content of the event.

[0582] "Activity schedule" refers to the specific program and session timetable for the event, and the detailed, systematically organized progress of the event.

[0583] "Personalized information" refers to data and suggestions customized for each participant, addressing their individual interests and preferences.

[0584] A "detector" is a device installed within an event venue that monitors and collects information about participants' movements and actions in real time.

[0585] An "AI dialogue system" is a system that uses natural language processing technology to communicate interactively with participants and provide information and support.

[0586] "Facial expression analysis function" is a technology that analyzes the facial expressions of participants, evaluates their emotional state, and is used to adjust the content of events.

[0587] This invention describes embodiments for carrying it out. The system is configured to automate and optimize the collection and analysis of participant information, the execution of events, and individualized responses to participants.

[0588] The server first collects participant information using a database management system (e.g., a Relational Database Management System). The data collected includes basic information about each participant, their past participation history, and survey results. This data is then analyzed using data analysis tools (e.g., Apache Hadoop or Spark) to provide foundational information for understanding participants' interests and trends.

[0589] Furthermore, the server analyzes historical and trending information. Trending information is obtained from internet data streams and industry reports. Based on this analysis, the server generates an event concept, for example, themed "AI and Ethics." This theme is expected to attract the interest of participants.

[0590] Based on the generated event theme, the server automatically organizes the activity schedule using a scheduling algorithm. This algorithm takes into account available resources (e.g., time slots, instructors, venue size) to provide an efficient and engaging timetable. Optimized scripts are used, leveraging a programming language (e.g., Python).

[0591] Multiple terminals will be installed throughout the venue, utilizing sensors and cameras. These devices will monitor participants' movements in real time and perform movement pattern analysis. This monitoring data will be processed by image analysis software (e.g., OpenCV) to provide information for inferring participants' interests and preferences.

[0592] In interacting with participants, the terminal communicates with them through an AI dialogue system. Using natural language processing technology, it analyzes participants' questions and emotional expressions, and provides appropriate responses and suggestions based on that analysis. For example, if a participant is unsure whether to visit a particular booth, the AI ​​dialogue system might suggest, "Why not take this opportunity to visit?" to pique their interest. This system dynamically integrates with each participant's profile information to provide more relevant information.

[0593] As a concrete example, the prompt text when using a generative AI model to devise a theme for a new event would be as follows:

[0594] Prompt message:

[0595] "Based on past technology conference data and the latest technology trends, please propose five interesting session titles and content ideas for a conference focused on next-generation technologies."

[0596] This configuration allows the system to provide a flexible and personalized event experience that reflects participants' real-time situations and emotions.

[0597] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0598] Step 1:

[0599] The server imports participant information into a database management system. This requires basic participant information, participation history, and survey results as input. The server organizes this data and generates participant profiles using data analysis tools. The output is profile data showing each participant's interests and tendencies.

[0600] Step 2:

[0601] The server analyzes historical and trending information. This process uses data from past events and current events information from the internet as input. Utilizing data analysis tools, the server creates event themes using a generative AI model. Specifically, it suggests themes such as "next-generation technologies" that reflect current trends. The output is the proposed event theme.

[0602] Step 3:

[0603] The server automatically creates an activity schedule based on the generated event theme. The required inputs are theme information and available resources (time slots, instructors, venue information). A scheduling algorithm optimizes resource allocation and generates a detailed timetable. The output is a completed event schedule.

[0604] Step 4:

[0605] The terminal uses sensors and cameras placed throughout the venue to monitor participants' movements in real time. Input data from the sensors includes participants' movements and location information. The terminal uses image analysis software to generate movement data. The output is trend data that reflects participants' interests and preferences.

[0606] Step 5:

[0607] The server receives real-time behavioral data sent from terminals and analyzes participants' emotions. The input data includes information based on participants' movements. An emotion analysis engine is used to identify participants' emotional states. The output is participant emotion evaluation data.

[0608] Step 6:

[0609] The terminal interacts with participants via an AI dialogue system. Input requires natural language questions and requests from participants, and sentiment evaluation data is also utilized. The terminal uses natural language processing technology to generate appropriate responses and provides them to participants in real time. The output is a personalized response provided to the participant.

[0610] Step 7:

[0611] The server facilitates networking by matching participants based on their shared interests. The input consists of participant profile data and sentiment rating data. A matchmaking algorithm identifies common interests among participants and generates combinations that support networking. The output is information on compatible participant pairings.

[0612] (Application Example 2)

[0613] 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."

[0614] In today's retail industry, there is a demand for providing appropriate information that meets the diverse needs and interests of consumers. However, traditional offline stores find it difficult to instantly optimize the individual customer experience. As a result, there are limitations to improving customer satisfaction and stimulating purchasing intent. To solve these problems, it is necessary to provide personalized purchase suggestions based on real-time customer behavior and sentiment analysis.

[0615] 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.

[0616] In this invention, the server includes means for collecting participant data, analyzing it based on past activity information and trend information to generate event themes, automatically creating activity progress plans based on the generated themes, and providing personalized information based on information of individual participants. This makes it possible to provide product information that is tailored to the customer's interests and emotions in real time.

[0617] "Participant data" refers to information including attribute information, past behavioral history, interests, and preferences of each individual.

[0618] "Activity information" refers to information about events, activities, and store layouts at specific locations and times.

[0619] "Trend information" refers to information based on recent trends and public interest, and is a factor that influences consumers' purchasing decisions.

[0620] An "event theme" is a concept or idea designed as the core of an activity or event being held.

[0621] A "procedure plan" is a plan that defines the order and procedures for running a particular activity or event.

[0622] "Personalized information" refers to customized content and suggestions tailored to an individual's profile.

[0623] A "detection device" is a device that senses the movements and facial expressions of participants in real time and collects that data.

[0624] "Real-time information" refers to data or feedback that immediately reflects ongoing events or situations.

[0625] "Analyzing movement patterns" involves analyzing the routes and flow of participants as they move within the facility and understanding those patterns.

[0626] "Facial recognition technology" is a technology used to identify and authenticate individuals based on their individual faces.

[0627] This invention is a system for analyzing customer behavior and emotions in retail stores and providing personalized information. Specifically, it is implemented as follows:

[0628] The server first acquires real-time information from cameras and sensors installed within the store to collect participant data. These devices include Azure Kinect and motion sensors on a Raspberry Pi. The server analyzes the collected data to generate event themes and schedules based on participants' behavior and emotions. Machine learning libraries such as TensorFlow and PyTorch are used in the analysis to identify participants' interests.

[0629] The device provides personalized product information on a smartphone application based on analyzed personal data. This allows users to receive appropriate products and recommendations based on their behavior within the store. For example, if a customer spends a long time in the wine section, the application will immediately notify them of relevant wine promotions.

[0630] Furthermore, the generative AI model uses "Identify the product categories the customer is interested in and generate a list of recommended products based on their sentiment" as an example of a prompt, enhancing the personalized user experience. In this way, the server and terminal work together to provide information that meets individual needs in real time.

[0631] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0632] Step 1:

[0633] The server collects environmental data in real time from Azure Kinect and Raspberry Pi-based motion sensors installed within the store. Input is raw data acquired from camera images and motion sensors, and output is a recorded file of that data. This allows for the accumulation of information regarding customer location and movement.

[0634] Step 2:

[0635] The server analyzes the collected data using TensorFlow or PyTorch. The input is the environmental data collected in step 1, and the output is the analysis results showing the customer's current interest categories and emotional state. The data is processed by machine learning algorithms to identify customer behavior patterns.

[0636] Step 3:

[0637] The terminal provides personalized product information as a push notification to the user's smartphone based on the analysis results received from the server. The input is the analysis results from step 2, and the output is the notification message on the smartphone. This operation allows the user to receive real-time suggestions for products that match their interests within the store.

[0638] Step 4:

[0639] Users receive notifications from their devices and check out interesting product information. Input is push notifications from the device, and output is the user's reactions and feedback. This information is then sent to the server and used for future analysis.

[0640] Step 5:

[0641] The server utilizes a generative AI model based on user response data to generate more refined product suggestions based on specified prompts such as, "Identify the product categories the customer is interested in and generate a list of recommended products based on their sentiment." The input is user feedback data, and the output is new personalized suggestions. The machine learning model is continuously improved throughout this process.

[0642] 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.

[0643] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0644] 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.

[0645] [Fourth Embodiment]

[0646] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0647] 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.

[0648] 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).

[0649] 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.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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".

[0659] This invention provides a system that automatically generates event concepts based on event participant data, creates schedules based on those concepts, and provides personalized information to individual participants. The program processing of this system is described below in natural language, along with specific examples.

[0660] First, the server collects registration information from event participants and past event data, and analyzes trends and participant feedback. Based on this analysis, it proposes an event concept, for example, one related to "next-generation technology." This determines the direction of the event and the content of each session.

[0661] Next, the server automatically creates the event date and a detailed session schedule based on the generated concept, taking into account resources (venue availability and speaker schedules). This enables efficient resource utilization and program planning that minimizes participant travel time.

[0662] The device notifies participants of customized schedules and session recommendations based on their individual profiles. For example, a participant interested in AI technology will be notified of details and times for relevant technical sessions. This allows participants to efficiently experience content that matches their interests.

[0663] Furthermore, the server uses sensors and facial recognition technology installed throughout the venue to analyze participants' movements in real time. Based on the data obtained, it can identify popular booths and crowded areas, and suggest optimal routes and destinations to participants.

[0664] Furthermore, the terminals enable interactive dialogue with participants through an AI chatbot to respond to their inquiries. For example, if a participant asks, "Which session is the most popular?", the chatbot can provide accurate information based on real-time data.

[0665] In this way, the server and terminals work together to provide an optimized event experience for each participant. This system makes it possible to achieve both efficient event management and increased participant satisfaction.

[0666] The following describes the processing flow.

[0667] Step 1:

[0668] The server collects historical event data and trend information. This includes information such as the number of event attendees, participant feedback, and interest in sessions.

[0669] Step 2:

[0670] The server analyzes the collected data. Machine learning algorithms are used to identify patterns from past successful events and automatically generate new event concepts.

[0671] Step 3:

[0672] The server creates a schedule based on the generated event concept. It efficiently arranges the program, taking into account venue availability, speaker schedules, and other factors.

[0673] Step 4:

[0674] The device accesses participant profile information. Based on registration information and past participation history, it identifies sessions and booths relevant to the participant's interests and needs.

[0675] Step 5:

[0676] The device notifies participants of a customized schedule. Programs reflecting participants' interests are delivered via push notifications and email to support efficient participation.

[0677] Step 6:

[0678] The server monitors participants' movements in real time through sensors and facial recognition cameras within the venue. This allows for the identification of crowded areas and analysis of popular booths.

[0679] Step 7:

[0680] The server matches participants with other participants and relevant content based on their interests, facilitating networking among participants with shared interests.

[0681] Step 8:

[0682] The terminal utilizes an AI chatbot to respond to participant inquiries. It provides participants with the information they need in real time, ensuring a smooth event experience.

[0683] By combining these steps, an optimized event experience is provided for participants, and the efficiency of event management is improved.

[0684] (Example 1)

[0685] 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".

[0686] Traditional event management has faced challenges due to the inefficiency of manually adjusting information to suit each participant's interests and schedule. Furthermore, the inability to optimize participant flow in real time, making it difficult to alleviate congestion, and limited immediate access to the latest information were also problematic.

[0687] 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.

[0688] In this invention, the server includes means for collecting participant data and analyzing it based on data and trend information related to past gatherings to generate a gathering concept; means for automatically creating a gathering schedule based on the generated concept; and means for providing individually tailored information based on information about each participant. This enables efficient management of the entire event and the provision of information optimized for each individual participant.

[0689] "Participant data" refers to information about individuals participating in the event, including their name, contact information, areas of interest, and past participation history.

[0690] "Data on past gatherings" refers to information about events held previously, including elements such as the number of participants, program content, and feedback.

[0691] "Trend information" refers to information based on the latest technological and social trends related to events, and is useful in determining the theme and content of future events.

[0692] The "concept of a gathering" refers to the central theme or concept of an event, derived from the interests and trend information of the participants.

[0693] The "meeting schedule" refers to a plan that includes the date and time of the event, the program structure, and the time schedule for each session.

[0694] "Individually tailored information based on each participant's information" refers to customized content provided to suit each participant's preferences, interests, and schedule.

[0695] A "detector" is a device installed within an event venue to track and analyze the location and movement of participants in real time.

[0696] "Individual recognition technology" refers to technology for identifying participants, and includes technology that identifies individuals using facial recognition or other recognition means.

[0697] An "intelligent program" is software that performs natural language processing and has the ability to provide information and answer questions by interacting with event participants.

[0698] "Means for coordinating spatial arrangement and speaker schedules based on the theme of the generated gathering" refers to the process of efficiently determining the venue layout and optimizing speaker participation times based on the event theme.

[0699] "Means for optimizing participants' movement routes and areas of interest" refers to the process of efficiently guiding participants within a venue and providing recommended behavioral patterns tailored to their individual interests.

[0700] This invention is a system that collects event participant data and provides individually tailored information to deliver an optimal experience to each participant.

[0701] First, the server collects and stores participant data using a database management system. Specifically, MySQL is used as the database, and libraries such as Python's pandas and scikit-learn are used for data analysis. The server analyzes past event data and trend information and inputs prompts into the generative AI model. An example of such a prompt is, "Generate event themes based on recent technology trends." Based on the output of this AI, a new concept of gathering is proposed.

[0702] Next, the server uses the Google Calendar API to automatically create event dates based on the generated group concept. The server employs linear programming to create the most efficient schedule when coordinating venue space and speaker schedules, ensuring participants can attend the event without wasting time.

[0703] The terminal notifies participants of schedule information sent from the server. Based on the participant's profile information, the terminal recommends the most suitable sessions and exhibits for each participant. For example, it can send specific notifications via a mobile app, such as, "The AI ​​technology session recommended for you starts at 2 PM."

[0704] Furthermore, the server uses IoT sensors and individual recognition technology installed within the venue to analyze participants' movements in real time. For example, it can use Azure's facial recognition API to identify popular areas and congested areas, and based on this, suggest the optimal route for movement.

[0705] Furthermore, the device implements an AI chatbot as an intelligent program, responding to participants' questions in real time. When a user asks, "What are the key points of this session?", it can provide detailed information based on the latest data via a generative AI model.

[0706] In this way, by effectively coordinating the server and terminals, it becomes possible to create an event experience optimized for each individual participant, thereby increasing satisfaction for both participants and the event.

[0707] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0708] Step 1:

[0709] The server collects data registered by participants and stores it in a database. It receives participants' personal information, past participation history, and areas of interest as input. The server stores this data in a database system such as MySQL. The output is data organized as individual participant profile information.

[0710] Step 2:

[0711] The server analyzes past data and trend information. It uses historical event data and trend information for input to a generative AI model. The server performs data analysis using Python's pandas and scikit-learn libraries. The output is a prompt message, "Generate event themes based on recent technology trends," to be sent to the generative AI model.

[0712] Step 3:

[0713] The server generates event themes using a generative AI model. It sends prompt text to the AI ​​as input. The server receives the event concepts output by the generative AI model and determines a theme, such as "next-generation technology." The output is the selected event theme.

[0714] Step 4:

[0715] The server automatically creates a schedule based on the event's theme. It takes the event theme, venue layout, and speaker schedules as input from the Google Calendar API. The server uses an optimization algorithm to calculate an efficient schedule and outputs a timetable.

[0716] Step 5:

[0717] The device notifies participants of the schedule information. As input, it receives personalized schedule information for each participant obtained from the server. The device uses a mobile app to send push notifications to participants, outputting a message such as, "The recommended AI technology session starts at 2 PM."

[0718] Step 6:

[0719] The server analyzes participant movement patterns using detectors and individual recognition technology within the venue. It collects real-time data from IoT sensors and facial recognition cameras as input. The server analyzes this data to identify popular areas and congested zones within the venue. The output is a suggested route for participants, incorporating this information.

[0720] Step 7:

[0721] The terminal interacts with participants through an intelligent program. It receives questions from participants as input. Based on information analyzed by an AI chatbot, the terminal provides a detailed answer to the question, "What are the key points of this session?" The output is the immediate provision of the information the participant is seeking.

[0722] (Application Example 1)

[0723] 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".

[0724] Providing an event experience tailored to the individual interests of participants and users in large-scale events and content distribution services is challenging. Furthermore, there is a lack of real-time participant behavior tracking and effective information provision based on that feedback. Additionally, there is a need for methods to improve participant satisfaction by suggesting personalized content based on viewing history and other factors.

[0725] 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.

[0726] In this invention, the server includes means for collecting participant information, analyzing it based on past data and market trends to generate an event overview, automatically creating a schedule based on the generated overview, and providing customized information based on the attributes of individual participants. This makes it possible to provide a personalized event experience tailored to the interests of each participant.

[0727] "Participants" refer to individual people who participate in events or content distribution services with a specific interest or purpose.

[0728] "Collecting information" refers to the act of gathering and organizing data about participants and past trend information.

[0729] "Event Overview" refers to a general plan that outlines the concept and structure of future events, based on the analyzed data.

[0730] "Automatically creating a schedule" refers to the process of mechanically organizing a schedule based on the elements of the generated event overview.

[0731] "Customized information" refers to data and content optimized for the specific interests and needs of individual participants.

[0732] "Personalized content" refers to media and information that are individually suggested based on a user's past viewing history and attribute information.

[0733] "Real-time information" refers to dynamic data that is acquired instantly without any time delay.

[0734] "Analyzing the route" refers to a method of examining participants' movement and location information in detail to identify patterns of movement.

[0735] A "popular area" refers to a specific location that attracts a large number of participants and shows a high level of interest.

[0736] "Personal identification technology" refers to algorithms and devices used to identify individuals.

[0737] The server first collects participant information and performs analysis based on past data and market trends. This stage utilizes programming languages ​​such as Python and data analysis software. Based on the analysis results, an event outline is generated, and then the schedule is automatically created. Resource management tools and scheduling software are used for schedule creation.

[0738] Subsequently, the server uses AI technology to provide customized information based on each participant's profile information. Machine learning models and natural language processing techniques are used to suggest personalized content tailored to individual interests.

[0739] Real-time data analysis utilizes dynamic information acquired from devices within the location. Personal identification technologies, such as facial recognition algorithms and sensors, are used to identify participants' routes and popular areas. This enables the design of efficient and comfortable movement patterns.

[0740] As a concrete example, user A is presented with a "SF movie festival" event that matches their viewing history using this system. Furthermore, the AI ​​dialogue system provides session information tailored to the participant's interests, facilitating interaction.

[0741] An example of a prompt to input into the generation AI model is: "Please provide details about the feature that automatically generates new content events in genres the user is interested in and notifies them with a personalized schedule."

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] The server uses participant information as input to retrieve each participant's past participation history and market trend information from the database. This gathers the necessary initial data for the event. This data is then used in subsequent analysis. The analysis program identifies different trends and generates an overview of the new event.

[0745] Step 2:

[0746] The server receives the event summary obtained in Step 1 as input and automatically creates a schedule using scheduling software. In this step, resource allocation and timetable adjustments are made based on the event concept, and the optimal event schedule is output.

[0747] Step 3:

[0748] The server collects participant profile information as input and generates personalized content based on interests using an AI model. This results in customized information being output for each user, with relevant events and media being recommended.

[0749] Step 4:

[0750] To achieve real-time analysis, the server acquires input data from devices within the location and uses facial recognition technology to analyze participants' movement patterns and dwell time. Based on this analysis data, information indicating popular areas and efficient traffic flow design is output.

[0751] Step 5:

[0752] The device receives information from the server and notifies the user of personalized event schedules and content relevant to participants. This notification function works to deliver information and sessions that the user is most likely to be interested in at the optimal time.

[0753] Step 6:

[0754] Based on the notifications they receive, users can participate in events and actively interact with them. Through an AI dialogue system, user questions are answered instantly, and information is provided in line with the progress of the event.

[0755] 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.

[0756] This invention provides a system that automatically generates event concepts based on participant data, creates a schedule based on those concepts, recognizes participants' emotions using an emotion engine, and provides personalized information. The program processing of this system is explained below in natural language, with specific examples.

[0757] First, the server collects participant information for the event and analyzes past event data and the latest trend information. This generates new event concepts, such as "a conference on next-generation technologies." Based on these concepts, the server automatically organizes the detailed program for the event, taking into account available resources and schedules.

[0758] Next, devices within the event venue use sensors and cameras to monitor participants' movements in real time, and the collected data is analyzed on a server. This allows the emotion engine to identify the participants' emotional states, such as whether they are happy or have lost interest.

[0759] For example, if many participants in a session begin to lose interest and leave, the server can detect this and adjust the topic of the next session to match the participants' interests. This dynamic content adjustment allows for flexible responses to changing participant needs.

[0760] Furthermore, the terminals use an AI chatbot to respond to participants' real-time inquiries in an emotionally responsive manner. For example, if a participant is unsure about which booth to choose, and the emotion engine detects that the participant is not interested, the chatbot will suggest the content in a positive and engaging way.

[0761] Furthermore, the server uses a matchmaking algorithm to facilitate networking based on shared interests among participants. The emotion engine analyzes the emotional responses of participants' conversations to support compatible pairings.

[0762] By incorporating participants' real-time emotional changes in this way, it becomes possible to create more personalized event management and improve participant satisfaction.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] The server collects registration information from event participants. This includes participants' names, topics of interest, and past event participation history.

[0766] Step 2:

[0767] The server analyzes past event data and the latest trend information to generate new event concepts. This process uses data mining techniques and machine learning algorithms to identify promising themes and content.

[0768] Step 3:

[0769] Based on the generated concept, the server automatically creates an event schedule, taking into account venue availability and speaker schedules. The schedule includes dates, times, and session details.

[0770] Step 4:

[0771] The device uses sensors and cameras within the venue to monitor participants' movements and emotional states in real time. It collects data such as participants' movement, time spent in the venue, and facial expressions.

[0772] Step 5:

[0773] The server analyzes real-time data it collects and uses an emotion engine to recognize the participants' emotions. For example, it can determine whether they are enjoying themselves or have lost interest.

[0774] Step 6:

[0775] The device provides personalized information based on the participant's emotional state. This information includes recommendations for sessions that might interest the participant and suggestions for travel routes.

[0776] Step 7:

[0777] The server uses an AI chatbot to provide emotion-based responses to participant inquiries. It also offers suggestions to make the content more engaging for participants who show little interest.

[0778] Step 8:

[0779] The server uses sentiment data to effectively match participants. It combines participants with similar sentiments and interests to facilitate networking.

[0780] This enables dynamic event management that takes emotions into account, thereby improving the participants' experience.

[0781] (Example 2)

[0782] 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".

[0783] In current event management, there is a challenge in understanding the diverse needs and interests of participants in real time and adjusting content accordingly. Furthermore, effectively utilizing changes in participants' emotions and behavior to provide personalized experiences is not easy. Therefore, it is necessary to maximize participant satisfaction while simultaneously improving operational efficiency.

[0784] 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.

[0785] In this invention, the server includes means for collecting participant information, analyzing past and trend information to generate event themes, automatically creating activity schedules based on the generated themes, and providing personalized information based on individual participant information. This enables flexible program adjustments in response to changes in participants' interests and emotions, and the provision of personalized, high-quality experiences.

[0786] "Participant information" refers to data related to individual users participating in the event, including basic information, past participation history, and trends.

[0787] "Past information" refers to a collection of data from previously held events and related information, which serves as material for analyzing trends and patterns.

[0788] "Trend information" refers to data on new social or industry trends and popular themes that can be useful in developing event concepts.

[0789] An "event theme" refers to the main idea or concept of a particular event, which is intended to attract participants' interest and guide the content of the event.

[0790] "Activity schedule" refers to the specific program and session timetable for the event, and the detailed, systematically organized progress of the event.

[0791] "Personalized information" refers to data and suggestions customized for each participant, addressing their individual interests and preferences.

[0792] A "detector" is a device installed within an event venue that monitors and collects information about participants' movements and actions in real time.

[0793] An "AI dialogue system" is a system that uses natural language processing technology to communicate interactively with participants and provide information and support.

[0794] "Facial expression analysis function" is a technology that analyzes the facial expressions of participants, evaluates their emotional state, and is used to adjust the content of events.

[0795] This invention describes embodiments for carrying it out. The system is configured to automate and optimize the collection and analysis of participant information, the execution of events, and individualized responses to participants.

[0796] The server first collects participant information using a database management system (e.g., a Relational Database Management System). The data collected includes basic information about each participant, their past participation history, and survey results. This data is then analyzed using data analysis tools (e.g., Apache Hadoop or Spark) to provide foundational information for understanding participants' interests and trends.

[0797] Furthermore, the server analyzes historical and trending information. Trending information is obtained from internet data streams and industry reports. Based on this analysis, the server generates an event concept, for example, themed "AI and Ethics." This theme is expected to attract the interest of participants.

[0798] Based on the generated event theme, the server automatically organizes the activity schedule using a scheduling algorithm. This algorithm takes into account available resources (e.g., time slots, instructors, venue size) to provide an efficient and engaging timetable. Optimized scripts are used, leveraging a programming language (e.g., Python).

[0799] Multiple terminals will be installed throughout the venue, utilizing sensors and cameras. These devices will monitor participants' movements in real time and perform movement pattern analysis. This monitoring data will be processed by image analysis software (e.g., OpenCV) to provide information for inferring participants' interests and preferences.

[0800] In interacting with participants, the terminal communicates with them through an AI dialogue system. Using natural language processing technology, it analyzes participants' questions and emotional expressions, and provides appropriate responses and suggestions based on that analysis. For example, if a participant is unsure whether to visit a particular booth, the AI ​​dialogue system might suggest, "Why not take this opportunity to visit?" to pique their interest. This system dynamically integrates with each participant's profile information to provide more relevant information.

[0801] As a concrete example, the prompt text when using a generative AI model to devise a theme for a new event would be as follows:

[0802] Prompt message:

[0803] "Based on past technology conference data and the latest technology trends, please propose five interesting session titles and content ideas for a conference focused on next-generation technologies."

[0804] This configuration allows the system to provide a flexible and personalized event experience that reflects participants' real-time situations and emotions.

[0805] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0806] Step 1:

[0807] The server imports participant information into a database management system. This requires basic participant information, participation history, and survey results as input. The server organizes this data and generates participant profiles using data analysis tools. The output is profile data showing each participant's interests and tendencies.

[0808] Step 2:

[0809] The server analyzes historical and trending information. This process uses data from past events and current events information from the internet as input. Utilizing data analysis tools, the server creates event themes using a generative AI model. Specifically, it suggests themes such as "next-generation technologies" that reflect current trends. The output is the proposed event theme.

[0810] Step 3:

[0811] The server automatically creates an activity schedule based on the generated event theme. The required inputs are theme information and available resources (time slots, instructors, venue information). A scheduling algorithm optimizes resource allocation and generates a detailed timetable. The output is a completed event schedule.

[0812] Step 4:

[0813] The terminal uses sensors and cameras placed throughout the venue to monitor participants' movements in real time. Input data from the sensors includes participants' movements and location information. The terminal uses image analysis software to generate movement data. The output is trend data that reflects participants' interests and preferences.

[0814] Step 5:

[0815] The server receives real-time behavioral data sent from terminals and analyzes participants' emotions. The input data includes information based on participants' movements. An emotion analysis engine is used to identify participants' emotional states. The output is participant emotion evaluation data.

[0816] Step 6:

[0817] The terminal interacts with participants via an AI dialogue system. Input requires natural language questions and requests from participants, and sentiment evaluation data is also utilized. The terminal uses natural language processing technology to generate appropriate responses and provides them to participants in real time. The output is a personalized response provided to the participant.

[0818] Step 7:

[0819] The server facilitates networking by matching participants based on their shared interests. The input consists of participant profile data and sentiment rating data. A matchmaking algorithm identifies common interests among participants and generates combinations that support networking. The output is information on compatible participant pairings.

[0820] (Application Example 2)

[0821] 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".

[0822] In today's retail industry, there is a demand for providing appropriate information that meets the diverse needs and interests of consumers. However, traditional offline stores find it difficult to instantly optimize the individual customer experience. As a result, there are limitations to improving customer satisfaction and stimulating purchasing intent. To solve these problems, it is necessary to provide personalized purchase suggestions based on real-time customer behavior and sentiment analysis.

[0823] 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.

[0824] In this invention, the server includes means for collecting participant data, analyzing it based on past activity information and trend information to generate event themes, automatically creating activity progress plans based on the generated themes, and providing personalized information based on information of individual participants. This makes it possible to provide product information that is tailored to the customer's interests and emotions in real time.

[0825] "Participant data" refers to information including attribute information, past behavioral history, interests, and preferences of each individual.

[0826] "Activity information" refers to information about events, activities, and store layouts at specific locations and times.

[0827] "Trend information" refers to information based on recent trends and public interest, and is a factor that influences consumers' purchasing decisions.

[0828] An "event theme" is a concept or idea designed as the core of an activity or event being held.

[0829] A "procedure plan" is a plan that defines the order and procedures for running a particular activity or event.

[0830] "Personalized information" refers to customized content and suggestions tailored to an individual's profile.

[0831] A "detection device" is a device that senses the movements and facial expressions of participants in real time and collects that data.

[0832] "Real-time information" refers to data or feedback that immediately reflects ongoing events or situations.

[0833] "Analyzing movement patterns" involves analyzing the routes and flow of participants as they move within the facility and understanding those patterns.

[0834] "Facial recognition technology" is a technology used to identify and authenticate individuals based on their individual faces.

[0835] This invention is a system for analyzing customer behavior and emotions in retail stores and providing personalized information. Specifically, it is implemented as follows:

[0836] The server first acquires real-time information from cameras and sensors installed within the store to collect participant data. These devices include Azure Kinect and motion sensors on a Raspberry Pi. The server analyzes the collected data to generate event themes and schedules based on participants' behavior and emotions. Machine learning libraries such as TensorFlow and PyTorch are used in the analysis to identify participants' interests.

[0837] The device provides personalized product information on a smartphone application based on analyzed personal data. This allows users to receive appropriate products and recommendations based on their behavior within the store. For example, if a customer spends a long time in the wine section, the application will immediately notify them of relevant wine promotions.

[0838] Furthermore, the generative AI model uses "Identify the product categories the customer is interested in and generate a list of recommended products based on their sentiment" as an example of a prompt, enhancing the personalized user experience. In this way, the server and terminal work together to provide information that meets individual needs in real time.

[0839] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0840] Step 1:

[0841] The server collects environmental data in real time from Azure Kinect and Raspberry Pi-based motion sensors installed within the store. Input is raw data acquired from camera images and motion sensors, and output is a recorded file of that data. This allows for the accumulation of information regarding customer location and movement.

[0842] Step 2:

[0843] The server analyzes the collected data using TensorFlow or PyTorch. The input is the environmental data collected in step 1, and the output is the analysis results showing the customer's current interest categories and emotional state. The data is processed by machine learning algorithms to identify customer behavior patterns.

[0844] Step 3:

[0845] The terminal provides personalized product information as a push notification to the user's smartphone based on the analysis results received from the server. The input is the analysis results from step 2, and the output is the notification message on the smartphone. This operation allows the user to receive real-time suggestions for products that match their interests within the store.

[0846] Step 4:

[0847] Users receive notifications from their devices and check out interesting product information. Input is push notifications from the device, and output is the user's reactions and feedback. This information is then sent to the server and used for future analysis.

[0848] Step 5:

[0849] The server utilizes a generative AI model based on user response data to generate more refined product suggestions based on specified prompts such as, "Identify the product categories the customer is interested in and generate a list of recommended products based on their sentiment." The input is user feedback data, and the output is new personalized suggestions. The machine learning model is continuously improved throughout this process.

[0850] 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.

[0851] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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."

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] The following is further disclosed regarding the embodiments described above.

[0872] (Claim 1)

[0873] A means of collecting participant data, analyzing it based on past event data and trend information, and generating an event concept.

[0874] A means to automatically create an event schedule based on the generated concept,

[0875] Means of providing personalized information based on the individual participant's profile,

[0876] A method for understanding participants' movements by analyzing real-time data from sensors within the venue,

[0877] A means of matching participants with each other and with event content,

[0878] A means of providing interactive dialogue with participants using an AI chatbot,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, further comprising means for notifying participants of the generated event schedule and providing them with information on sessions and booths that may be of interest to them.

[0882] (Claim 3)

[0883] The system according to claim 1, further comprising means for analyzing the movement of participants using sensors and facial recognition technology within the venue to identify popular areas and crowded areas.

[0884] "Example 1"

[0885] (Claim 1)

[0886] A means of collecting participant data, analyzing it based on past data and trend information about gatherings, and generating a concept of a gathering.

[0887] A means of automatically creating a meeting schedule based on the generated concept,

[0888] Means of providing information tailored individually based on the information of each participant,

[0889] A means of understanding participants' movements by analyzing real-time data from detectors within the venue,

[0890] Means for ensuring consistency among participants and between participants and the content of the gathering,

[0891] A means of providing interactive dialogue with participants using an intelligent program,

[0892] A means for coordinating spatial arrangement and speaker schedules based on the theme of the generated gathering,

[0893] Means for optimizing participants' movement paths and areas of interest,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, further comprising means for notifying participants of the generated meeting schedule and providing information on topics and exhibits that may be of interest to the participants.

[0897] (Claim 3)

[0898] The system according to claim 1, further comprising means for analyzing the movement paths of participants using detectors and individual recognition technology within the venue to identify popular areas and crowded areas.

[0899] "Application Example 1"

[0900] (Claim 1)

[0901] A means of collecting participant information, analyzing it based on past data and market trends to generate an event overview,

[0902] A means of automatically creating a schedule based on the generated outline,

[0903] Means of providing customized information based on the attributes of individual participants,

[0904] A means of understanding participants' behavior by analyzing real-time information from devices within the location,

[0905] Means for ensuring compatibility between participants and between participants and the event content,

[0906] A means of providing dialogue with participants using an AI dialogue system,

[0907] A means of providing personalized content suggestions based on the user's viewing history,

[0908] Means to facilitate participation in and interaction with generated virtual events,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, further comprising means for notifying participants of the generated schedule and providing them with information on programs and locations that may be of interest to them.

[0912] (Claim 3)

[0913] The system according to claim 1, further comprising means for analyzing the paths of participants using devices and personal recognition technology within the location to identify popular areas and crowded areas.

[0914] "Example 2 of combining an emotion engine"

[0915] (Claim 1)

[0916] It has a function to collect participant information, analyze past and trend information to generate event themes,

[0917] A feature that automatically creates activity schedules based on generated themes,

[0918] Features that provide personalized information based on individual participant information,

[0919] The system analyzes real-time information from sensors within the venue to understand the movements of participants,

[0920] Features that establish relationships between participants and between participants and event content,

[0921] A function that provides interaction with participants using an AI dialogue system,

[0922] A function that analyzes participants' emotions and dynamically adjusts the content,

[0923] Functions that promote good relationships among participants,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, further comprising a function to notify participants of the generated activity schedule and to provide them with activity content that they may be interested in.

[0927] (Claim 3)

[0928] The system according to claim 1, further comprising a function to analyze the movement of participants using sensors and facial expression analysis functions within the venue to identify popular areas and crowded areas.

[0929] "Application example 2 when combining with an emotional engine"

[0930] (Claim 1)

[0931] A means of collecting participant data, analyzing it based on past activity information and trend information to generate event themes,

[0932] A means for automatically creating an activity progress plan based on the generated theme,

[0933] Means of providing personalized information based on information of individual participants,

[0934] A means of understanding participants' movements by analyzing real-time information from detection devices within the location,

[0935] A means of matching participants with each other and with the activities,

[0936] A means of providing interaction with participants using an AI chat automated response system,

[0937] By analyzing trends and emotions, a means of estimating participants' interests and providing promotional information for products,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, further comprising means for notifying participants of the generated activity plan and for providing information that may be of interest to the participants.

[0941] (Claim 3)

[0942] The system according to claim 1, further comprising means for analyzing the movement of participants using detection devices and facial recognition technology within the location to identify popular areas and crowded areas. [Explanation of symbols]

[0943] 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. A means of collecting participant information, analyzing it based on past data and market trends to generate an event overview, A means to automatically create a schedule based on the generated outline, Means of providing customized information based on the attributes of individual participants, A means of understanding participants' behavior by analyzing real-time information from devices within the location, Means for ensuring compatibility between participants and between participants and the event content, A means of providing dialogue with participants using an AI dialogue system, A means of providing personalized content suggestions based on the user's viewing history, Means to facilitate participation in and interaction with generated virtual events, A system that includes this.

2. The system according to claim 1, further comprising means for notifying participants of the generated schedule and providing them with information on programs and locations that may be of interest to them.

3. The system according to claim 1, further comprising means for analyzing the paths of participants using devices and personal recognition technology within the location to identify popular areas and crowded areas.

Images