System

Abstract

An object of a system according to an embodiment is to provide information on an event in real time and to quickly respond to a question or a question of a participant.SOLUTION: A system according to an embodiment includes an information providing unit, a question handling unit, and an automatic updating unit. The information providing unit provides information on the event in real time. The question answering unit quickly answers the participant's question or question. The automatic updating unit automatically updates the information about the event.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that information about events and communication with participants is not carried out smoothly, and information updates can be delayed.

[0005] The system according to the embodiment aims to provide information about an event in real time and to quickly respond to participants' doubts and questions. [Means for solving the problem]

[0006] The system according to the embodiment includes an information providing unit, a question handling unit, and an automatic updating unit. The information providing unit provides information about the event in real time. The question handling unit promptly responds to questions and doubts from participants. The automatic updating unit automatically updates the information about the event. [Effects of the Invention]

[0007] The system according to the embodiment provides information about the event in real time and can quickly respond to participants' doubts and questions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) An event information provision system according to an embodiment of the present invention utilizes an AI chatbot to provide event-related information in real time and quickly respond to participants' questions and concerns. This allows event organizers to quickly provide appropriate information to participants, ensuring the smooth running of the event. Participants can easily obtain information about the event, reducing stress and anxiety and allowing them to participate in a more enjoyable event.

[0029] An event information providing system according to an embodiment includes an information providing unit, a question response unit, and an automatic update unit. The information providing unit provides information about an event in real time. For example, the information providing unit provides basic information such as the event schedule, location, and participation method. The information providing unit also updates changes and additional information occurring during the event in real time and notifies participants. For example, the information generated during the event, such as schedule changes or the addition of a new session, is updated in real time and notifies participants. The question response unit promptly responds to participants' doubts and questions. For example, participants can ask questions anonymously through an AI chatbot. For example, the generation AI provides appropriate answers to questions such as, "What time does the next session start?" or "Where is the venue?" The automatic update unit automatically updates information about the event. For example, an event organizer can use an AI chatbot to automatically update information about the event. For example, the automatic update unit updates information generated during the event, such as schedule changes or the addition of a new session, in real time and notifies participants. This allows the event information providing system according to an embodiment to quickly provide appropriate information to participants and ensure the smooth running of the event. Participants can also easily obtain information about the event, reducing stress and anxiety and allowing them to participate in a more enjoyable event.

[0030] The information providing unit can provide individually customized information based on the past participation history or interests of event participants. For example, the information providing unit stores the past participation history of event participants in a database and provides customized information based on that history at the next event. For example, information related to the contents of sessions previously attended or topics of interest is preferentially provided. The information providing unit also provides information on related sessions and activities during the event based on interests and concerns registered in advance by participants. For example, a participant who is interested in a particular field is notified of information on sessions related to that field. The information providing unit also builds a system that provides more appropriate information at the next event based on participants' past feedback and ratings. For example, information related to sessions that have received high ratings in the past is preferentially provided. This makes it possible to provide more appropriate information to participants.

[0031] The information providing unit can predict participants' behavior according to the progress of the event and proactively provide them with the information they will need next. For example, the information providing unit monitors the progress of the event in real time, predicts participants' behavior, and provides them with the information they will need next. For example, after a session ends, it notifies them of the start time and location of the next session. The information providing unit also analyzes participants' behavioral patterns and builds a system that proactively provides the information they will need next. For example, when a break approaches, it provides information about rest areas and restaurants. The information providing unit also develops an algorithm that automatically provides the information participants will need next according to the progress of the event. For example, it provides related materials and links as the session progresses. This makes it possible to provide participants with the information they need in a timely manner.

[0032] The information providing unit can add a voice assistant function to enable information acquisition by voice. For example, the information providing unit adds a voice assistant function to an AI Chatbot to enable participants to acquire information by voice. For example, the start time and location of the next session can be confirmed by voice command. The information providing unit also uses the voice assistant function to build a system that notifies participants of changes and additional information that occur during an event by voice. For example, schedule changes can be notified by voice. The information providing unit also provides a function that allows participants to input questions by voice and the AI ​​Chatbot to respond by voice. For example, participants can ask, "What time does the next session start?" and receive a response by voice. This allows participants to acquire information by voice.

[0033] The information providing unit can send event information to participants' smart devices via push notifications, thereby enhancing real-time information provision. For example, the information providing unit can send event information to participants' smart devices via push notifications, thereby enhancing real-time information provision. For example, a push notification can be used to notify participants of changes in the start time or location of a session. The information providing unit can also send push notifications to participants' smart devices to build a system that quickly conveys important information and emergency notices. For example, it can notify participants of emergency announcements or important changes. The information providing unit can also use push notifications to provide participants with information they need in a timely manner. For example, it can send a reminder before the start of the next session. This allows information to be provided to participants in real time.

[0034] The question response unit can automatically provide related additional information or reference materials depending on the content of the question. For example, the question response unit builds a system that analyzes the content of participants' questions and automatically provides related additional information and reference materials. For example, in response to the question, "What time does the next session start?", detailed information about the session and related materials are provided. The question response unit also automatically provides related links and documents depending on the content of the question. For example, in response to the question, "Where is the venue?", a map of the venue and access information are provided. The question response unit also develops a system that automatically provides information about related sessions and activities based on the content of the question. For example, in response to the question, "Who is the speaker in the next session?", speaker profiles and information about related sessions are provided. This makes it possible to provide related information in response to participants' questions.

[0035] The question response unit can analyze the frequency or content of questions and automatically generate and provide frequently asked questions as FAQs. For example, the question response unit builds a system that analyzes the frequency and content of questions and automatically generates frequently asked questions as FAQs. For example, it automatically extracts frequently asked questions and adds them to an FAQ page. The question response unit also analyzes the content of the questions and groups related questions to provide them as FAQs. For example, it posts questions about the same topic together in the FAQ. The question response unit also develops a system that updates FAQs in real time based on the frequency and content of questions. For example, it immediately adds newly asked questions to the FAQ and provides them to participants. This allows participants to quickly obtain answers to frequently asked questions.

[0036] The question response unit can provide visual answers using the images or videos in response to questions, thereby deepening understanding. The question response unit, for example, builds a system that provides visual answers using images or videos depending on the content of the question. For example, in response to the question, "Where is the venue?", a map of the venue and a video of how to access it can be provided. The question response unit also uses visual answers to deepen participants' understanding. For example, in response to the question, "Who is the speaker for the next session?", a photo of the speaker and an introductory video can be provided. The question response unit also develops a system that automatically generates visual answers using images and videos. For example, it searches for related images and videos depending on the content of the question and incorporates them into the answer. This can deepen participants' understanding.

[0037] The question response unit can add a function that analyzes the content of questions and shares the information as useful to other participants. The question response unit, for example, builds a system that analyzes the content of questions and shares the information as useful to other participants. For example, it automatically extracts frequently asked questions and posts them on a shared page. The question response unit also provides related information to other participants based on the content of the question. For example, in response to the question, "What time does the next session start?", it notifies all participants of the start time of the session. The question response unit also analyzes the content of questions and develops a system that shares information that is useful to other participants in real time. For example, it immediately adds a newly received question to a shared page and notifies all participants. This makes it possible to share useful information with all participants.

[0038] The automatic update unit can automatically update information in real time according to the progress of the event or changes in the external environment. For example, the automatic update unit builds a system that monitors the progress of the event in real time and automatically updates information according to changes in the external environment. For example, the schedule of an outdoor event is changed according to changes in the weather. The automatic update unit also analyzes changes in the external environment and automatically updates event information in real time. For example, access information to the venue is updated according to changes in traffic conditions. The automatic update unit also develops an algorithm that automatically updates necessary information according to the progress of the event. For example, related materials and links are provided as the session progresses. This allows event information to be kept always up to date.

[0039] The automatic update unit can have the generation AI automatically optimize information manually updated by the event organizer and provide it to participants. For example, the automatic update unit builds a system in which the generation AI automatically optimizes information manually updated by the event organizer and provides it to participants. For example, when the organizer changes the schedule, the generation AI optimizes the information and notifies participants. The automatic update unit also analyzes the information manually updated by the organizer and provides it to participants in the optimal format. For example, it converts text information into images and graphs to provide it visually. The automatic update unit also develops a system in which the generation AI extracts the information most important to participants based on the information manually updated by the organizer and provides it to them preferentially. For example, it highlights important changes and notifies them. This allows the information manually updated by the organizer to be optimized and provided to participants.

[0040] The automatic update unit can simultaneously update event information across multiple platforms (SNS, email, apps) and provide information widely. The automatic update unit, for example, builds a system that simultaneously updates event information across multiple platforms. For example, it can simultaneously distribute information across SNS, email, and apps, providing information widely to participants. The automatic update unit also links multiple platforms and updates event information all at once. For example, once the organizer updates information, it is automatically reflected on all platforms. The automatic update unit also develops a system that distributes event information across multiple platforms in real time and provides participants with the latest information. For example, it can immediately notify schedule changes via SNS or email. This allows event information to be provided widely.

[0041] The automatic update unit can link event information updates with participants' calendar apps and automatically reflect them in their schedules. The automatic update unit, for example, builds a system that links event information updates with participants' calendar apps and automatically reflects them in their schedules. For example, changes to session start times and locations are automatically reflected in the calendar. The automatic update unit also links with the calendar app to update event information in real time. For example, when a participant opens the calendar app, the latest event information is displayed. The automatic update unit also develops a system that automatically adds event information to the calendar app, making it easier for participants to manage their schedules. For example, when a new session is added, it is automatically reflected in the calendar. This allows event information to be automatically reflected in participants' schedules.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The event information system can also obtain participants' location information and provide information based on their location. For example, when a participant approaches a specific venue, it can notify them of information about sessions taking place at that venue. It can also provide navigation functions based on the location information to prevent participants from getting lost. For example, it can display a map of the venue and guide participants on the route from their current location to their destination. It can also use the location information to provide relevant information based on the amount of time participants spend in a particular area. For example, participants who stay in the same area for a long time can be provided with information about nearby rest areas and restaurants. This allows participants to move smoothly within the event venue and obtain the information they need in a timely manner.

[0044] The event information system can also link with participants' social media accounts to provide customized information based on their reactions and posts on social media. For example, if a participant posts about a specific event, information related to that event will be provided preferentially. It can also analyze reactions on social media to provide information about sessions and activities related to topics that interest participants. For example, participants who use a specific hashtag will be notified of information related to that hashtag. It can also build a system that provides more appropriate information for future events based on feedback on social media. For example, it can analyze past posts and provide preferentially information related to topics that participants have highly rated. This makes it possible to provide more personalized information to participants.

[0045] Event information provision systems can also predict and proactively provide the information participants will need next based on their behavioral history. For example, they can analyze behavioral patterns from past events and provide information about sessions and activities that participants are likely to be interested in next. They can also provide information about places participants are likely to visit next based on their movement history. For example, after a particular session ends, they can be notified of the place participants are likely to head to next. They can also use behavioral history to develop algorithms that automatically provide the next information participants will need. For example, they can provide the next materials or links they will need based on past behavioral patterns. This makes it possible to provide participants with the information they need in a timely manner.

[0046] Event information systems can also add a voice assistant function, allowing participants to obtain information by voice. For example, a voice assistant function can be added to an AI chatbot, allowing participants to obtain information by voice. For example, they can use voice commands to confirm the start time and location of the next session. The voice assistant function can also be used to create a system that notifies participants of changes or additional information that occur during the event by voice. For example, schedule changes can be notified by voice. It is also possible to provide a function where participants can input questions by voice and the AI ​​chatbot will respond by voice. For example, they can ask, "What time does the next session start?" and receive a voice response. This allows participants to obtain information by voice.

[0047] The event information provision system can also send information to participants' smart devices via push notifications to enhance real-time information provision. For example, event information can be sent to participants' smart devices via push notifications to enhance real-time information provision. For example, push notifications can be used to notify participants of changes in session start times or locations. A system can also be built that sends push notifications to participants' smart devices to quickly convey important information or emergency notices. For example, it can notify participants of emergency announcements or important changes. Push notifications can also be used to provide participants with the information they need in a timely manner. For example, a reminder can be sent before the start of the next session. This makes it possible to provide participants with information in real time.

[0048] The event information providing system can furthermore automatically provide related additional information or reference materials in the question response section according to the content of the question. For example, a system can be constructed that analyzes the content of participants' questions and automatically provides related additional information or reference materials. For example, in response to the question, "What time does the next session start?", detailed information about the session and related materials can be provided. It is also possible to automatically provide related links and documents according to the content of the question. For example, in response to the question, "Where is the venue?", a map of the venue and access information can be provided. It is also possible to develop a system that automatically provides information about related sessions and activities based on the content of the question. For example, in response to the question, "Who is the speaker in the next session?", speaker profiles and information about related sessions can be provided. This makes it possible to provide related information in response to participants' questions.

[0049] The event information provision system can further analyze the frequency or content of questions in its question response section, and automatically generate and provide frequently asked questions as FAQs. For example, a system can be built that analyzes the frequency and content of questions and automatically generates frequently asked questions as FAQs. For example, frequently asked questions can be automatically extracted and added to an FAQ page. It can also analyze the content of questions and group related questions to provide them as FAQs. For example, questions about the same topic can be posted together in the FAQ. It can also develop a system that updates FAQs in real time based on the frequency and content of questions. For example, newly asked questions can be immediately added to the FAQ and provided to participants. This allows participants to quickly get answers to frequently asked questions.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The information provider provides information about the event in real time. For example, basic information such as the event schedule, location, and participation method is provided. It also updates any changes or additional information that occurs during the event in real time and notifies participants. For example, it updates information that occurs during the event, such as schedule changes or the addition of new sessions, in real time and notifies participants. Step 2: The Q&A department responds quickly to participants' doubts and questions. For example, participants can ask questions anonymously through the AI ​​Chatbot. For example, the generative AI will provide appropriate answers to questions such as, "What time does the next session start?" or "Where is the venue?" Step 3: The automatic update unit automatically updates information about the event. For example, event organizers can use AI chatbots to automatically update information about the event. For example, information occurring during the event, such as schedule changes or the addition of new sessions, can be updated in real time and notified to participants.

[0052] (Example 2) An event information provision system according to an embodiment of the present invention utilizes an AI chatbot to provide event-related information in real time and quickly respond to participants' questions and concerns. This allows event organizers to quickly provide appropriate information to participants, ensuring the smooth running of the event. Participants can easily obtain information about the event, reducing stress and anxiety and allowing them to participate in a more enjoyable event.

[0053] An event information providing system according to an embodiment includes an information providing unit, a question response unit, and an automatic update unit. The information providing unit provides information about an event in real time. For example, the information providing unit provides basic information such as the event schedule, location, and participation method. The information providing unit also updates changes and additional information occurring during the event in real time and notifies participants. For example, the information generated during the event, such as schedule changes or the addition of a new session, is updated in real time and notifies participants. The question response unit promptly responds to participants' doubts and questions. For example, participants can ask questions anonymously through an AI chatbot. For example, the generation AI provides appropriate answers to questions such as, "What time does the next session start?" or "Where is the venue?" The automatic update unit automatically updates information about the event. For example, an event organizer can use an AI chatbot to automatically update information about the event. For example, the automatic update unit updates information generated during the event, such as schedule changes or the addition of a new session, in real time and notifies participants. This allows the event information providing system according to an embodiment to quickly provide appropriate information to participants and ensure the smooth running of the event. Participants can also easily obtain information about the event, reducing stress and anxiety and allowing them to participate in a more enjoyable event.

[0054] The information providing unit can provide individually customized information based on the past participation history or interests of event participants. For example, the information providing unit stores the past participation history of event participants in a database and provides customized information based on that history at the next event. For example, information related to the contents of sessions previously attended or topics of interest is preferentially provided. The information providing unit also provides information on related sessions and activities during the event based on interests and concerns registered in advance by participants. For example, a participant who is interested in a particular field is notified of information on sessions related to that field. The information providing unit also builds a system that provides more appropriate information at the next event based on participants' past feedback and ratings. For example, information related to sessions that have received high ratings in the past is preferentially provided. This makes it possible to provide more appropriate information to participants.

[0055] The information providing unit can predict participants' behavior according to the progress of the event and proactively provide them with the information they will need next. For example, the information providing unit monitors the progress of the event in real time, predicts participants' behavior, and provides them with the information they will need next. For example, after a session ends, it notifies them of the start time and location of the next session. The information providing unit also analyzes participants' behavioral patterns and builds a system that proactively provides the information they will need next. For example, when a break approaches, it provides information about rest areas and restaurants. The information providing unit also develops an algorithm that automatically provides the information participants will need next according to the progress of the event. For example, it provides related materials and links as the session progresses. This makes it possible to provide participants with the information they need in a timely manner.

[0056] The information providing unit uses the emotion estimation function to provide information according to the emotional state of participants, thereby reducing stress. For example, the information providing unit analyzes the emotional state of participants in real time and provides information to help them relax if they are under high stress. For example, it may provide information about break times or suggest ways to refresh. The information providing unit also uses the emotion estimation function to provide information about the next activity if a participant is excited, thereby maintaining their interest. For example, it may provide highlights of the next session or introduce the speaker. The information providing unit also builds a system that provides customized information according to the emotional state of participants. For example, it may provide relaxing content to participants who are nervous, giving them a sense of security. This reduces stress for participants and makes their event participation more enjoyable.

[0057] The information providing unit can add a voice assistant function to enable information acquisition by voice. For example, the information providing unit adds a voice assistant function to an AI Chatbot to enable participants to acquire information by voice. For example, the start time and location of the next session can be confirmed by voice command. The information providing unit also uses the voice assistant function to build a system that notifies participants of changes and additional information that occur during an event by voice. For example, schedule changes can be notified by voice. The information providing unit also provides a function that allows participants to input questions by voice and the AI ​​Chatbot to respond by voice. For example, participants can ask, "What time does the next session start?" and receive a response by voice. This allows participants to acquire information by voice.

[0058] The information providing unit can send event information to participants' smart devices via push notifications, thereby enhancing real-time information provision. For example, the information providing unit can send event information to participants' smart devices via push notifications, thereby enhancing real-time information provision. For example, a push notification can be used to notify participants of changes in the start time or location of a session. The information providing unit can also send push notifications to participants' smart devices to build a system that quickly conveys important information and emergency notices. For example, it can notify participants of emergency announcements or important changes. The information providing unit can also use push notifications to provide participants with information they need in a timely manner. For example, it can send a reminder before the start of the next session. This allows information to be provided to participants in real time.

[0059] The information providing unit can use the emotion estimation function to provide participants with information that interests them most on a priority basis. The information providing unit, for example, uses the emotion estimation function to build a system that provides participants with information that interests them most on a priority basis. For example, it prioritizes notifying participants of information about sessions related to topics of interest. The information providing unit also analyzes the emotional state of participants and provides them with information that interests them most on a priority basis. For example, it provides excited participants with information about the next activity to maintain their interest. The information providing unit also develops a system that provides participants with information that interests them most in real time based on the emotion estimation data. For example, it prioritizes notifying participants of information related to topics with high emotion scores. This makes it possible to provide participants with information that interests them most on a priority basis.

[0060] The question response unit can automatically provide related additional information or reference materials depending on the content of the question. For example, the question response unit builds a system that analyzes the content of participants' questions and automatically provides related additional information and reference materials. For example, in response to the question, "What time does the next session start?", detailed information about the session and related materials are provided. The question response unit also automatically provides related links and documents depending on the content of the question. For example, in response to the question, "Where is the venue?", a map of the venue and access information are provided. The question response unit also develops a system that automatically provides information about related sessions and activities based on the content of the question. For example, in response to the question, "Who is the speaker in the next session?", speaker profiles and information about related sessions are provided. This makes it possible to provide related information in response to participants' questions.

[0061] The question response unit can analyze the frequency or content of questions and automatically generate and provide frequently asked questions as FAQs. For example, the question response unit builds a system that analyzes the frequency and content of questions and automatically generates frequently asked questions as FAQs. For example, it automatically extracts frequently asked questions and adds them to an FAQ page. The question response unit also analyzes the content of the questions and groups related questions to provide them as FAQs. For example, it posts questions about the same topic together in the FAQ. The question response unit also develops a system that updates FAQs in real time based on the frequency and content of questions. For example, it immediately adds newly asked questions to the FAQ and provides them to participants. This allows participants to quickly obtain answers to frequently asked questions.

[0062] The question response unit uses the emotion estimation function to provide an answer that corresponds to the emotional state of the questioner, giving them a sense of security. The question response unit, for example, uses the emotion estimation function to analyze the emotional state of the questioner and builds a system that provides an answer that gives them a sense of security. For example, a questioner who is nervous is provided with an answer that helps them relax. The question response unit also provides an answer that is customized according to the questioner's emotional state. For example, a questioner who is highly stressed is provided with an answer in kind words, giving them a sense of security. The question response unit also develops a system that provides an answer that matches the emotional state of the questioner based on the emotion estimation data. For example, a questioner with a low emotion score is provided with an answer that includes words of encouragement. This makes it possible to give the questioner a sense of security.

[0063] The question response unit can provide visual answers using the images or videos in response to questions, thereby deepening understanding. The question response unit, for example, builds a system that provides visual answers using images or videos depending on the content of the question. For example, in response to the question, "Where is the venue?", a map of the venue and a video of how to access it can be provided. The question response unit also uses visual answers to deepen participants' understanding. For example, in response to the question, "Who is the speaker for the next session?", a photo of the speaker and an introductory video can be provided. The question response unit also develops a system that automatically generates visual answers using images and videos. For example, it searches for related images and videos depending on the content of the question and incorporates them into the answer. This can deepen participants' understanding.

[0064] The question response unit can add a function that analyzes the content of questions and shares the information as useful to other participants. The question response unit, for example, builds a system that analyzes the content of questions and shares the information as useful to other participants. For example, it automatically extracts frequently asked questions and posts them on a shared page. The question response unit also provides related information to other participants based on the content of the question. For example, in response to the question, "What time does the next session start?", it notifies all participants of the start time of the session. The question response unit also analyzes the content of questions and develops a system that shares information that is useful to other participants in real time. For example, it immediately adds a newly received question to a shared page and notifies all participants. This makes it possible to share useful information with all participants.

[0065] The question response unit can use the emotion estimation function to automatically send a follow-up message according to the emotion of the questioner. The question response unit, for example, uses the emotion estimation function to analyze the emotional state of the questioner and builds a system that automatically sends a follow-up message. For example, if the questioner is emotionally unstable after asking a question, an encouraging message is sent. The question response unit also automatically sends a customized follow-up message according to the questioner's emotional state. For example, a message that helps the questioner relax is sent to a questioner who is highly stressed. The question response unit also develops a system that automatically sends a follow-up message tailored to the questioner's emotional state based on the emotion estimation data. For example, a message with words of encouragement is sent to a questioner with a low emotion score. This makes it possible to provide appropriate follow-up to the questioner.

[0066] The automatic update unit can automatically update information in real time according to the progress of the event or changes in the external environment. For example, the automatic update unit builds a system that monitors the progress of the event in real time and automatically updates information according to changes in the external environment. For example, the schedule of an outdoor event is changed according to changes in the weather. The automatic update unit also analyzes changes in the external environment and automatically updates event information in real time. For example, access information to the venue is updated according to changes in traffic conditions. The automatic update unit also develops an algorithm that automatically updates necessary information according to the progress of the event. For example, related materials and links are provided as the session progresses. This allows event information to be kept always up to date.

[0067] The automatic update unit can have the generation AI automatically optimize information manually updated by the event organizer and provide it to participants. For example, the automatic update unit builds a system in which the generation AI automatically optimizes information manually updated by the event organizer and provides it to participants. For example, when the organizer changes the schedule, the generation AI optimizes the information and notifies participants. The automatic update unit also analyzes the information manually updated by the organizer and provides it to participants in the optimal format. For example, it converts text information into images and graphs to provide it visually. The automatic update unit also develops a system in which the generation AI extracts the information most important to participants based on the information manually updated by the organizer and provides it to them preferentially. For example, it highlights important changes and notifies them. This allows the information manually updated by the organizer to be optimized and provided to participants.

[0068] The automatic updating unit can use the emotion estimation function to analyze participants' reactions and adjust the content or format of information as necessary. The automatic updating unit, for example, uses the emotion estimation function to build a system that analyzes participants' reactions in real time and adjusts the content and format of information. For example, if a participant is feeling stressed, it provides information in a concise summary. The automatic updating unit also analyzes the participants' emotional state and adjusts the format of the information. For example, it provides detailed information to excited participants and provides summary information to relaxed participants. The automatic updating unit also develops a system that adjusts the content of the information according to the participants' reactions based on the emotion estimation data. For example, if the emotion score is low, it provides information containing positive elements. This makes it possible to optimize the information according to the participants' reactions.

[0069] The automatic update unit can simultaneously update event information across multiple platforms (SNS, email, apps) and provide information widely. The automatic update unit, for example, builds a system that simultaneously updates event information across multiple platforms. For example, it can simultaneously distribute information across SNS, email, and apps, providing information widely to participants. The automatic update unit also links multiple platforms and updates event information all at once. For example, once the organizer updates information, it is automatically reflected on all platforms. The automatic update unit also develops a system that distributes event information across multiple platforms in real time and provides participants with the latest information. For example, it can immediately notify schedule changes via SNS or email. This allows event information to be provided widely.

[0070] The automatic update unit can link event information updates with participants' calendar apps and automatically reflect them in their schedules. The automatic update unit, for example, builds a system that links event information updates with participants' calendar apps and automatically reflects them in their schedules. For example, changes to session start times and locations are automatically reflected in the calendar. The automatic update unit also links with the calendar app to update event information in real time. For example, when a participant opens the calendar app, the latest event information is displayed. The automatic update unit also develops a system that automatically adds event information to the calendar app, making it easier for participants to manage their schedules. For example, when a new session is added, it is automatically reflected in the calendar. This allows event information to be automatically reflected in participants' schedules.

[0071] The automatic update unit uses the emotion estimation function to optimize the timing or frequency of information updates, thereby reducing stress for participants. The automatic update unit, for example, uses the emotion estimation function to build a system that optimizes the timing and frequency of information updates. For example, if a participant is feeling stressed, the frequency of information updates is reduced. The automatic update unit also analyzes the emotional state of the participant and adjusts the timing of information updates. For example, information is updated during times when the participant is relaxed, thereby reducing stress. The automatic update unit also develops a system that determines the optimal timing and frequency of information updates based on emotion estimation data. For example, if the emotion score is low, information updates are refrained from. This makes it possible to optimize the timing and frequency of information updates to reduce stress for participants.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The event information system can also obtain participants' location information and provide information based on their location. For example, when a participant approaches a specific venue, it can notify them of information about sessions taking place at that venue. It can also provide navigation functions based on the location information to prevent participants from getting lost. For example, it can display a map of the venue and guide participants on the route from their current location to their destination. It can also use the location information to provide relevant information based on the amount of time participants spend in a particular area. For example, participants who stay in the same area for a long time can be provided with information about nearby rest areas and restaurants. This allows participants to move smoothly within the event venue and obtain the information they need in a timely manner.

[0074] The event information system can also link with participants' social media accounts to provide customized information based on their reactions and posts on social media. For example, if a participant posts about a specific event, information related to that event will be provided preferentially. It can also analyze reactions on social media to provide information about sessions and activities related to topics that interest participants. For example, participants who use a specific hashtag will be notified of information related to that hashtag. It can also build a system that provides more appropriate information for future events based on feedback on social media. For example, it can analyze past posts and provide preferentially information related to topics that participants have highly rated. This makes it possible to provide more personalized information to participants.

[0075] Event information provision systems can also predict and proactively provide the information participants will need next based on their behavioral history. For example, they can analyze behavioral patterns from past events and provide information about sessions and activities that participants are likely to be interested in next. They can also provide information about places participants are likely to visit next based on their movement history. For example, after a particular session ends, they can be notified of the place participants are likely to head to next. They can also use behavioral history to develop algorithms that automatically provide the next information participants will need. For example, they can provide the next materials or links they will need based on past behavioral patterns. This makes it possible to provide participants with the information they need in a timely manner.

[0076] The event information provision system can also analyze the emotional state of participants and provide information appropriate to their emotions. For example, if a participant is feeling stressed, it can provide them with information to help them relax. For example, it can provide information about break times and suggest ways to refresh themselves. If a participant is excited, it can provide them with information about the next activity to maintain their interest. For example, it can provide highlights of the next session or an introduction to the speaker. It is also possible to build a system that provides customized information depending on their emotional state. For example, it can provide relaxing content to participants who are nervous, giving them a sense of security. This can reduce participants' stress and make their event participation more enjoyable.

[0077] Event information systems can also add a voice assistant function, allowing participants to obtain information by voice. For example, a voice assistant function can be added to an AI chatbot, allowing participants to obtain information by voice. For example, they can use voice commands to confirm the start time and location of the next session. The voice assistant function can also be used to create a system that notifies participants of changes or additional information that occur during the event by voice. For example, schedule changes can be notified by voice. It is also possible to provide a function where participants can input questions by voice and the AI ​​chatbot will respond by voice. For example, they can ask, "What time does the next session start?" and receive a voice response. This allows participants to obtain information by voice.

[0078] The event information provision system can also send information to participants' smart devices via push notifications to enhance real-time information provision. For example, event information can be sent to participants' smart devices via push notifications to enhance real-time information provision. For example, push notifications can be used to notify participants of changes in session start times or locations. A system can also be built that sends push notifications to participants' smart devices to quickly convey important information or emergency notices. For example, it can notify participants of emergency announcements or important changes. Push notifications can also be used to provide participants with the information they need in a timely manner. For example, a reminder can be sent before the start of the next session. This makes it possible to provide participants with information in real time.

[0079] The event information provision system can further use the emotion estimation function to prioritize providing information that participants are most interested in. For example, a system can be built using the emotion estimation function to prioritize providing information that participants are most interested in. For example, information about sessions related to topics of interest can be prioritized. It is also possible to analyze the emotional state of participants and prioritize providing information that is of high interest to them. For example, excited participants can be provided with information about the next activity to maintain their interest. It is also possible to develop a system that provides information that participants are most interested in in real time based on the emotion estimation data. For example, information related to topics with high emotion scores can be prioritized. This makes it possible to prioritize providing information that participants are most interested in.

[0080] The event information providing system can furthermore automatically provide related additional information or reference materials in the question response section according to the content of the question. For example, a system can be constructed that analyzes the content of participants' questions and automatically provides related additional information or reference materials. For example, in response to the question, "What time does the next session start?", detailed information about the session and related materials can be provided. It is also possible to automatically provide related links and documents according to the content of the question. For example, in response to the question, "Where is the venue?", a map of the venue and access information can be provided. It is also possible to develop a system that automatically provides information about related sessions and activities based on the content of the question. For example, in response to the question, "Who is the speaker in the next session?", speaker profiles and information about related sessions can be provided. This makes it possible to provide related information in response to participants' questions.

[0081] The event information provision system can further use an emotion estimation function in the question response section to provide an answer that corresponds to the emotional state of the questioner, giving them a sense of security. For example, a system can be constructed that uses the emotion estimation function to analyze the emotional state of the questioner and provide an answer that gives them a sense of security. For example, an answer that helps a nervous questioner to relax can be provided. It is also possible to provide an answer that is customized according to the questioner's emotional state. For example, a questioner who is highly stressed can be answered in kind words, giving them a sense of security. It is also possible to develop a system that provides an answer that matches the questioner's emotional state based on the emotion estimation data. For example, an answer that includes encouraging words can be provided to a questioner with a low emotion score. This can give the questioner a sense of security.

[0082] The event information provision system can further analyze the frequency or content of questions in its question response section, and automatically generate and provide frequently asked questions as FAQs. For example, a system can be built that analyzes the frequency and content of questions and automatically generates frequently asked questions as FAQs. For example, frequently asked questions can be automatically extracted and added to an FAQ page. It can also analyze the content of questions and group related questions to provide them as FAQs. For example, questions about the same topic can be posted together in the FAQ. It can also develop a system that updates FAQs in real time based on the frequency and content of questions. For example, newly asked questions can be immediately added to the FAQ and provided to participants. This allows participants to quickly get answers to frequently asked questions.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The information provider provides information about the event in real time. For example, basic information such as the event schedule, location, and participation method is provided. It also updates any changes or additional information that occurs during the event in real time and notifies participants. For example, it updates information that occurs during the event, such as schedule changes or the addition of new sessions, in real time and notifies participants. Step 2: The Q&A department responds quickly to participants' doubts and questions. For example, participants can ask questions anonymously through the AI ​​Chatbot. For example, the generative AI will provide appropriate answers to questions such as, "What time does the next session start?" or "Where is the venue?" Step 3: The automatic update unit automatically updates information about the event. For example, event organizers can use AI chatbots to automatically update information about the event. For example, information occurring during the event, such as schedule changes or the addition of new sessions, can be updated in real time and notified to participants.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information provider that provides real-time information about events; A Q&A department that responds quickly to questions and concerns from participants, An automatic update unit that automatically updates information about events. A system characterized by:

2. The information providing unit According to the progress of the event, the actions of the participants are predicted and the next necessary information is provided in advance.

2. The system of claim 1.

3. The information providing unit Adds voice assistant functionality, allowing users to obtain information via voice 2. The system of claim 1.

4. The question response unit Automatically provide additional relevant information or references based on the question 2. The system of claim 1.

5. The automatic update unit The information is automatically updated in real time according to the progress of the event or changes in the external environment.

2. The system of claim 1.

6. The information providing unit Provide information according to the participant's emotional state to reduce stress 2. The system of claim 1.

7. The question response unit Providing answers that respond to the questioner's emotional state and provide reassurance 2. The system of claim 1.

8. The automatic update unit Analyze the participant's response and adjust the content or format of the information as needed.

2. The system of claim 1.

Images