system

Abstract

The system according to this embodiment aims to estimate the emotions of music festival participants in real time and propose live performances and activities based on that estimation. [Solution] The system according to the embodiment comprises an acquisition unit, an emotion estimation unit, and a suggestion unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of participants in a music festival. The emotion estimation unit estimates the emotions of the participants in real time based on the emotion estimation information acquired by the acquisition unit. The suggestion unit proposes live performances and activities based on the emotions estimated by the emotion estimation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to grasp in real time the feelings of participants in a music festival and propose appropriate live performances and activities based on them.

[0005] The system according to the embodiment aims to estimate in real time the feelings of participants in a music festival and propose live performances and activities based on them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a suggestion unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of participants in a music festival. The emotion estimation unit estimates the emotions of participants in real time based on the emotion estimation information acquired by the acquisition unit. The suggestion unit proposes live performances and activities based on the emotions estimated by the emotion estimation unit. [Effects of the Invention]

[0007] The system according to this embodiment can estimate the emotions of music festival participants in real time and suggest live performances and activities based on that. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The music festival experience enhancement robot system according to an embodiment of the present invention is a system that estimates participants' emotions in real time and proposes live performances and activities tailored to their preferences. This system acquires information for estimating participants' emotions and estimates their emotions in real time based on the acquired information. Based on the estimated emotions, it proposes live performances and activities. It also has a function to analyze emotion data and inform the audience of the venue's congestion status and waiting times in real time. Furthermore, it has a function to find other participants based on emotion data and promote interaction. For example, information for estimating participants' emotions can be obtained from the participant's facial expressions, tone of voice, heart rate, etc. For example, if a participant is smiling or speaking in an excited tone of voice, it is estimated that they have energetic emotions. This information is collected by the acquisition unit. Next, based on the acquired information, the system estimates the participant's emotions in real time. The emotion estimation unit analyzes the acquired information and estimates the participant's emotions. For example, if a participant is smiling, the emotion estimation unit estimates that the participant is feeling happy. This estimation includes AI processing. Based on the estimated emotions, it proposes live performances and activities. The suggestion unit proposes the most suitable live performances and activities for participants based on the emotions estimated by the emotion estimation unit. For example, if a participant is looking for energetic music, the suggestion unit will guide them to a lively performance. This suggestion also involves AI processing. Furthermore, it also has a function to analyze emotion data and inform participants of the venue's congestion level and waiting times in real time. The congestion notification unit informs participants of the venue's congestion level and waiting times in real time based on the emotions estimated by the emotion estimation unit. For example, if a participant wants to avoid crowds, the congestion notification unit will guide them to less crowded areas. This notification also involves AI processing. Finally, it also has a function to find other participants and facilitate interaction based on emotion data. The interaction facilitation unit finds other participants and facilitates interaction based on the emotions estimated by the emotion estimation unit. For example, if a participant wants to make new friends, the interaction facilitation unit will introduce them to other participants with similar emotions. This interaction also involves AI processing.This allows the music festival experience enhancement robot system to estimate participants' emotions in real time and suggest optimal live performances and activities.

[0029] The robot system for enhancing the music festival experience according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a suggestion unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of music festival participants. The acquisition unit acquires, for example, facial expression data of participants using a camera. The acquisition unit can also acquire the tone of voice of participants using a microphone. Furthermore, the acquisition unit can acquire the heart rate of participants using a sensor. For example, the acquisition unit analyzes the facial expressions of participants using facial recognition technology and acquires emotion estimation information. It can also analyze the tone of voice of participants using a microphone and acquire emotion estimation information. It can also measure the heart rate of participants using a sensor and acquire emotion estimation information. The emotion estimation unit estimates the emotions of participants in real time based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit analyzes the acquired information using, for example, AI and estimates the emotions of participants. For example, the emotion estimation unit analyzes the facial expressions of participants using facial recognition technology and estimates emotions. It can also analyze the tone of voice of participants using voice analysis technology and estimate emotions. The system can also analyze heart rate variability and estimate emotions. Based on the emotions estimated by the emotion estimation unit, the suggestion unit proposes live performances and activities. For example, the suggestion unit uses AI to suggest the most suitable live performances and activities based on the participant's emotions. For instance, if a participant is looking for energetic music, the suggestion unit will guide them to a lively performance. If a participant wants to relax, it can also suggest a calming activity. If a participant wants to make new friends, it can also suggest a social event. As a result, the music festival experience enhancement robot system according to this embodiment can estimate participants' emotions in real time and suggest the most suitable live performances and activities.

[0030] The acquisition unit acquires emotion estimation information, which is used to estimate the emotions of music festival participants. For example, the acquisition unit acquires participants' facial expression data using a camera. Specifically, the camera has high resolution and can capture subtle changes in facial expressions. This makes it possible to analyze participants' emotions such as joy, surprise, and sadness in detail. The acquisition unit can also acquire the tone of participants' voices using a microphone. The microphone is highly sensitive and can accurately capture changes in the volume and tone of participants' voices. This makes it possible to determine whether participants are excited or relaxed. Furthermore, the acquisition unit can acquire participants' heart rates using a sensor. Sensors come in various types, such as wristbands and chest straps, and can monitor heart rates in real time. For example, the acquisition unit analyzes participants' facial expressions using facial recognition technology and acquires emotion estimation information. The facial recognition technology employs a deep learning algorithm and can detect facial feature points with high accuracy. It can also analyze the tone of participants' voices using a microphone and acquire emotion estimation information. Voice analysis technology can analyze changes in the frequency components and volume of speech to capture changes in emotion. It can also measure participants' heart rates using sensors to acquire information for emotion estimation. Heart rate variability is an important indicator of a participant's level of excitement and stress, and analyzing this can improve the accuracy of emotion estimation. As a result, the acquisition unit can collect emotion estimation information from diverse sources, providing fundamental data for highly accurate estimation of participants' emotions.

[0031] The emotion estimation unit estimates the participant's emotions in real time based on emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit analyzes the acquired information using AI to estimate the participant's emotions. Specifically, the emotion estimation unit employs a neural network model using deep learning and comprehensively analyzes various data such as facial expressions, voice tone, and heart rate. For example, the emotion estimation unit analyzes the participant's facial expressions using facial recognition technology to estimate emotions. Facial recognition technology analyzes feature points such as the degree of eye opening, the degree of mouth corner upturning, and eyebrow movement, and can estimate emotions such as joy, surprise, sadness, and anger with high accuracy. It is also possible to analyze the participant's voice tone using voice analysis technology to estimate emotions. Voice analysis technology analyzes the frequency components, volume, and rhythm of the voice to estimate emotional states such as excitement, relaxation, and tension. It is also possible to analyze heart rate fluctuations to estimate emotions. Heart rate fluctuations are an important indicator of the degree of stress and excitement, and analyzing them can improve the accuracy of emotion estimation. Furthermore, the emotion estimation unit can improve the accuracy of emotion estimation by utilizing past data and statistical information. For example, it can learn patterns of emotional responses to specific music genres or performances based on data from past festival participants and reflect this in real-time emotion estimation. This allows the emotion estimation unit to quickly and accurately analyze acquired data and estimate participants' emotions in real time.

[0032] The suggestion unit proposes live performances and activities based on the emotions estimated by the emotion estimation unit. For example, the suggestion unit uses AI to suggest the most suitable live performances and activities based on the participant's emotions. Specifically, the suggestion unit analyzes the participant's emotional data and selects the performance or activity that best suits their current emotional state. For example, if a participant is looking for energetic music, the suggestion unit will recommend a lively performance. Energetic music often consists of fast-paced, rhythmically emphasized songs, which have the effect of increasing the participant's excitement. If a participant wants to relax, the suggestion unit can also suggest a calming activity. Relaxing music often consists of slow-paced, gently melodic songs, which have the effect of reducing the participant's stress. If a participant wants to make new friends, the suggestion unit can also suggest a social event. Social events provide an environment where participants can naturally start conversations with each other, helping them to build new relationships. Furthermore, the suggestion unit can make more personalized suggestions by considering the participant's past behavioral history and preferences. For example, it can make suggestions that best suit the current emotional state based on data of performances and activities that the participant has enjoyed participating in in the past. Furthermore, the proposal team can continuously revise its suggestions based on real-time updated sentiment data to provide the optimal experience. This allows the proposal team to suggest live performances and activities that are best suited to the participants' emotions, thereby enhancing the music festival experience.

[0033] The emotion estimation unit includes a congestion notification unit that informs participants of the congestion status or waiting time in the venue in real time based on the estimated emotion. The congestion notification unit uses AI, for example, to inform participants of the congestion status and waiting time in the venue in real time based on their emotions. For example, if a participant wants to avoid congestion, the congestion notification unit will guide them to a less crowded area. If a participant wants to reduce their waiting time, it can also suggest activities with shorter waiting times. The congestion notification unit uses AI, for example, to analyze the congestion status in the venue and notify participants in real time. This makes it possible to inform participants of the congestion status and waiting time in the venue in real time based on their emotions.

[0034] The emotion estimation unit includes an interaction facilitator that finds other participants and facilitates interaction based on the estimated emotions. The interaction facilitator, for example, uses AI to find other participants and facilitate interaction based on the participant's emotions. For example, if a participant feels like making new friends, the interaction facilitator will introduce them to other participants with similar emotions. If a participant has a particular interest, it can also find other participants with the same interest and facilitate interaction. The interaction facilitator, for example, uses AI to analyze the participant's emotional data and find the most suitable interaction partner. This allows it to find other participants and facilitate interaction based on the participant's emotions.

[0035] The acquisition unit acquires information for emotion estimation, such as the participant's facial expressions, voice tone, and heart rate. For example, the acquisition unit can acquire the participant's facial expressions using a camera. The acquisition unit can also acquire the participant's voice tone using a microphone. The acquisition unit can also acquire the participant's heart rate using a sensor. For example, the acquisition unit can analyze the participant's facial expressions using facial recognition technology and acquire information for emotion estimation. The acquisition unit can also analyze the participant's voice tone using voice analysis technology and acquire information for emotion estimation. The acquisition unit can also analyze fluctuations in heart rate and acquire information for emotion estimation. By acquiring information for emotion estimation, such as the participant's facial expressions, voice tone, and heart rate, the accuracy of emotion estimation is improved.

[0036] The suggestion unit proposes live performances and activities to participants based on the emotions estimated by the emotion estimation unit. For example, the suggestion unit uses AI to suggest the most suitable live performances and activities based on the participants' emotions. For instance, if a participant is looking for energetic music, the suggestion unit will recommend a lively performance. If a participant wants to relax, it can also suggest a calming activity. The suggestion unit analyzes the participants' emotional data using AI to make optimal suggestions. This allows it to propose the most suitable live performances and activities to participants based on the emotions estimated by the emotion estimation unit.

[0037] The data acquisition unit estimates the participant's emotions and adjusts the timing of acquiring emotion estimation information based on the estimated emotions. For example, the data acquisition unit uses AI to estimate the participant's emotions and adjusts the timing of acquiring emotion estimation information. For example, if the participant is excited, the data acquisition unit acquires emotion estimation information frequently to track changes in emotions in real time. If the participant is relaxed, the interval between acquiring emotion estimation information can be widened to save resources. For example, the data acquisition unit uses AI to analyze the participant's emotional data and determine the optimal acquisition timing. This allows for efficient use of resources by adjusting the timing of acquiring emotion estimation information based on the participant's emotions.

[0038] The data acquisition unit analyzes participants' past emotional data and selects the optimal acquisition method. For example, the data acquisition unit uses AI to analyze participants' past emotional data and select the optimal acquisition method. For example, if a participant has shown high emotional fluctuations during a particular event in the past, the data acquisition unit will prioritize acquiring information related to that event. It is also possible to analyze participants' past emotional data to identify periods when emotions tend to fluctuate and focus on acquiring information during those periods. For example, the data acquisition unit uses AI to analyze participants' past emotional data and determine the optimal acquisition method. This allows for the selection of the optimal acquisition method by analyzing participants' past emotional data, thereby improving the accuracy of emotion estimation.

[0039] The data acquisition unit filters the data for sentiment estimation based on the participant's current activities and areas of interest. For example, the data acquisition unit uses AI to filter the data based on the participant's current activities and areas of interest. For instance, if a participant is performing live, the data acquisition unit prioritizes acquiring information related to that performance. If a participant is participating in a specific activity, the data acquisition unit can also filter and acquire information related to that activity. For example, the data acquisition unit uses AI to analyze the participant's areas of interest and acquire the most relevant information. This allows for the acquisition of more relevant information by filtering the data based on the participant's current activities and areas of interest.

[0040] The data acquisition unit estimates the participant's emotions and determines the priority of emotion estimation information to acquire based on the estimated emotions. For example, the data acquisition unit uses AI to estimate the participant's emotions and determines the priority of emotion estimation information. For example, if the participant is excited, the data acquisition unit prioritizes acquiring physiological data such as heart rate and voice tone. If the participant is relaxed, it may also prioritize acquiring facial expression data. For example, the data acquisition unit uses AI to analyze the participant's emotional data and determine the optimal priority. This allows for the priority acquisition of important information by determining the priority of information based on the participant's emotions.

[0041] The data acquisition unit prioritizes acquiring highly relevant information by considering the participant's geographical location when acquiring information for sentiment estimation. For example, the data acquisition unit uses AI to consider the participant's geographical location when acquiring information for sentiment estimation. For example, if the participant is approaching a specific stage, the data acquisition unit prioritizes acquiring information related to that stage. If the participant is staying in a specific area, it can also prioritize acquiring information related to that area. For example, the data acquisition unit uses AI to analyze the participant's geographical location and acquire the most relevant information. This allows for the priority acquisition of highly relevant information by considering the participant's geographical location.

[0042] The data acquisition unit analyzes participants' social media activity and acquires relevant information when acquiring information for sentiment estimation. For example, the data acquisition unit uses AI to analyze participants' social media activity and acquire information for sentiment estimation. For example, if a participant mentions a specific artist on social media, the data acquisition unit prioritizes acquiring information related to that artist. If a participant mentions a specific event on social media, it can also prioritize acquiring information related to that event. For example, the data acquisition unit uses AI to analyze participants' social media activity and acquire the most relevant information. This allows for the priority acquisition of relevant information by analyzing participants' social media activity.

[0043] The emotion estimation unit estimates the participant's emotions and adjusts the emotion estimation algorithm based on the estimated emotions. For example, the emotion estimation unit uses AI to estimate the participant's emotions and adjusts the emotion estimation algorithm. For instance, if the participant is excited, the emotion estimation unit speeds up the emotion estimation algorithm to track emotional changes in real time. If the participant is relaxed, it can also slow down the emotion estimation algorithm to save resources. The emotion estimation unit uses AI to analyze the participant's emotional data and adjust the optimal algorithm. This allows for real-time tracking of emotional changes by adjusting the emotion estimation algorithm based on the participant's emotions.

[0044] The emotion estimation unit improves estimation accuracy by referring to the participant's past emotional data during emotion estimation. For example, the emotion estimation unit uses AI to refer to the participant's past emotional data and improve the accuracy of emotion estimation. For example, if the emotion estimation unit finds that a participant has shown high emotional fluctuations in a particular event in the past, it refers to that data to improve estimation accuracy. It can also analyze the participant's past emotional data to identify periods when emotions tend to fluctuate and refer to that data to improve estimation accuracy. For example, the emotion estimation unit uses AI to analyze the participant's past emotional data and select the optimal estimation method. This allows for improved accuracy of emotion estimation by referring to the participant's past emotional data.

[0045] The emotion estimation unit considers the participant's attribute information when estimating emotions. For example, the emotion estimation unit uses AI to consider the participant's attribute information and perform emotion estimation. For example, the emotion estimation unit adjusts the emotion estimation algorithm based on the participant's age to improve estimation accuracy. It can also adjust the emotion estimation algorithm based on the participant's gender to improve estimation accuracy. For example, the emotion estimation unit uses AI to analyze the participant's attribute information and select the optimal estimation method. This improves the accuracy of emotion estimation by considering the participant's attribute information.

[0046] The emotion estimation unit estimates the participant's emotions and adjusts the display method of the emotion estimation results based on the estimated emotions. For example, the emotion estimation unit uses AI to estimate the participant's emotions and adjusts the display method of the emotion estimation results. For example, if the participant is excited, the emotion estimation unit can provide a visually stimulating display method. If the participant is relaxed, it can also provide a calm display method. For example, the emotion estimation unit uses AI to analyze the participant's emotional data and determine the optimal display method. This allows for a visually appropriate display by adjusting the display method based on the participant's emotions.

[0047] The sentiment estimation unit considers the geographical distribution of participants when estimating sentiment. For example, the sentiment estimation unit uses AI to consider the geographical distribution of participants and perform sentiment estimation. For example, if participants are concentrated in a specific area, the sentiment estimation unit performs sentiment estimation based on information related to that area. If participants are widely dispersed, it can also perform sentiment estimation by integrating information from each area. For example, the sentiment estimation unit uses AI to analyze the geographical distribution of participants and select the optimal estimation method. This improves the accuracy of sentiment estimation by considering the geographical distribution of participants.

[0048] The sentiment estimation unit improves its estimation accuracy by referring to relevant literature and data during sentiment estimation. For example, the sentiment estimation unit uses AI to refer to relevant literature and data to improve the accuracy of sentiment estimation. For example, the sentiment estimation unit refers to the latest research papers on sentiment estimation and improves its algorithm. It can also improve the accuracy of sentiment estimation by referring to past event data. For example, the sentiment estimation unit uses AI to analyze the sentiment data of other participants and select the optimal estimation method. In this way, the accuracy of sentiment estimation can be improved by referring to relevant literature and data.

[0049] The proposal department estimates the participants' emotions and adjusts the presentation of the proposal based on those estimated emotions. For example, the proposal department might use AI to estimate the participants' emotions and adjust the presentation of the proposal. For instance, if a participant is excited, the proposal department might present a visually stimulating proposal. If a participant is relaxed, it might present a calmer proposal. The proposal department might use AI to analyze the participants' emotional data and determine the optimal presentation method. This allows for visually appropriate proposals by adjusting the presentation method based on the participants' emotions.

[0050] The proposal team adjusts the level of detail in their proposals based on the importance of the live performances and activities. For example, they might use AI to adjust the level of detail based on the importance of the live performances and activities. For instance, they might provide detailed information for important live performances, or concise information for less important activities. The proposal team might use AI to analyze participants' levels of interest and determine the optimal level of detail for each proposal. This allows for the provision of appropriate information by adjusting the level of detail based on the importance of the live performances and activities.

[0051] The proposal team applies different proposal algorithms depending on the category of the live performance or activity. For example, the proposal team uses AI to apply proposal algorithms according to the category of the live performance or activity. For instance, in the case of a music performance, the proposal team makes suggestions based on the music genre. In the case of an art exhibition, it can also make suggestions based on the exhibition content. For example, the proposal team uses AI to analyze the participants' areas of interest and select the optimal proposal algorithm. By applying different proposal algorithms depending on the category of the live performance or activity, more appropriate suggestions become possible.

[0052] The proposal team estimates the participant's emotions and adjusts the length of the proposal based on the estimated emotions. For example, the proposal team might use AI to estimate the participant's emotions and adjust the proposal length. For instance, if the participant is excited, the proposal team might provide a short, concise proposal. If the participant is relaxed, it might provide a longer proposal with more detailed explanations. The proposal team might use AI to analyze the participant's emotional data and determine the optimal proposal length. This allows for the provision of appropriate information by adjusting the proposal length based on the participant's emotions.

[0053] The proposal team prioritizes proposals based on the timing of live performances and activities. For example, they might use AI to prioritize proposals based on the timing of live performances and activities. For instance, they might prioritize proposing upcoming live performances. They might also prioritize proposing important activities. The proposal team might use AI to analyze participants' schedules and determine the optimal proposal priority. This allows for the provision of appropriate information by prioritizing proposals based on the timing of live performances and activities.

[0054] The suggestion department adjusts the order of suggestions based on the relevance of live performances and activities. For example, it uses AI to adjust the order of suggestions based on the relevance of live performances and activities. For example, the suggestion department prioritizes suggesting highly relevant performances based on participants' interests. It can also suggest highly relevant activities based on participants' past participation history. For example, the suggestion department uses AI to analyze participants' emotional data and determine the optimal suggestion order. This allows for the provision of appropriate information by adjusting the order of suggestions based on the relevance of live performances and activities.

[0055] The congestion notification unit estimates participants' emotions and adjusts the congestion notification method based on the estimated emotions. For example, the congestion notification unit uses AI to estimate participants' emotions and adjust the congestion notification method. For example, if a participant is excited, the congestion notification unit can provide a visually stimulating notification method. If a participant is relaxed, it can also provide a calm notification method. For example, the congestion notification unit uses AI to analyze participants' emotional data and determine the optimal notification method. This makes it possible to provide appropriate information by adjusting the congestion notification method based on participants' emotions.

[0056] The congestion notification unit predicts the current congestion level by referring to past congestion data when issuing congestion notifications. For example, the congestion notification unit uses AI to refer to past congestion data and predict the current congestion level. For example, the congestion notification unit predicts congestion levels for specific time periods based on past congestion data. It can also analyze congestion patterns in specific areas from past congestion data and predict the current congestion level. For example, the congestion notification unit uses AI to analyze past congestion data and select the optimal prediction method. This allows for the prediction of current congestion levels by referring to past congestion data, enabling the provision of appropriate information.

[0057] The congestion notification unit applies different notification methods to each area within the venue when notifying about congestion. For example, the congestion notification unit uses AI to apply different notification methods to each area within the venue. For example, if a particular area is congested, the congestion notification unit will prioritize notifying information related to that area. If a particular area is not crowded, it can also prioritize notifying information related to that area. For example, the congestion notification unit uses AI to analyze the congestion status of each area within the venue and select the optimal notification method. This makes it possible to provide appropriate information by applying different notification methods to each area within the venue.

[0058] The congestion notification unit estimates participants' emotions and adjusts the importance of congestion status based on the estimated emotions. For example, the congestion notification unit uses AI to estimate participants' emotions and adjust the importance of congestion status. For example, if a participant is excited, the congestion notification unit will prioritize notifying them of congestion status. If a participant is relaxed, it may also reduce the frequency of congestion status notifications. For example, the congestion notification unit uses AI to analyze participants' emotional data and determine the optimal importance. This allows for the provision of appropriate information by adjusting the importance of congestion status based on participants' emotions.

[0059] The congestion notification unit considers the geographical information of the area within the venue when issuing congestion notifications. For example, the congestion notification unit uses AI to consider the geographical information of the area within the venue when issuing congestion notifications. For example, if a particular area is congested, the congestion notification unit will issue a notification based on the geographical information of that area. It can also issue a notification based on the geographical information of a particular area if it is not crowded. For example, the congestion notification unit uses AI to analyze the geographical information of each area within the venue and select the optimal notification method. This makes it possible to provide appropriate information by considering the geographical information of the area within the venue.

[0060] The congestion notification unit improves notification accuracy by referring to relevant data when issuing congestion notifications. For example, the congestion notification unit uses AI to refer to relevant data and improve notification accuracy. For example, the congestion notification unit refers to past congestion data to improve notification accuracy. It can also refer to the sentiment data of other participants to improve notification accuracy. For example, the congestion notification unit uses AI to analyze real-time congestion data and select the optimal notification method. This improves notification accuracy and enables the provision of appropriate information by referring to relevant data.

[0061] The interaction facilitator estimates participants' emotions and adjusts interaction facilitator methods based on these estimates. For example, the interaction facilitator might use AI to estimate participants' emotions and adjust the methods accordingly. If a participant is excited, the interaction facilitator might provide visually stimulating interaction facilitator methods. If a participant is relaxed, it might provide calming interaction facilitator methods. The interaction facilitator might use AI to analyze participants' emotional data and determine the optimal interaction facilitator method. This allows for appropriate interaction facilitator methods to be adjusted based on participants' emotions.

[0062] The interaction promotion department selects the optimal interaction method by referring to participants' past interaction history when promoting interaction. For example, the interaction promotion department may use AI to refer to participants' past interaction history and select the optimal interaction method. For example, the interaction promotion department may select the optimal interaction method based on participants' past interaction history at specific events. It is also possible to analyze from participants' past interaction history whether interaction is active at certain times and focus interaction promotion on those times. For example, the interaction promotion department may use AI to analyze participants' past interaction history and select the optimal interaction method. This makes it possible to select the optimal interaction method and promote appropriate interaction by referring to participants' past interaction history.

[0063] The interaction promotion department considers participants' attribute information when facilitating interaction. For example, the department uses AI to consider participants' attribute information when facilitating interaction. For example, the department selects the optimal interaction method based on the participant's age. It can also select the optimal interaction method based on the participant's gender. For example, the department uses AI to analyze participants' attribute information and determine the optimal interaction method. This makes it possible to promote appropriate interaction by considering participants' attribute information.

[0064] The interaction promotion department estimates participants' emotions and determines the priority of interaction promotion based on those estimated emotions. For example, the interaction promotion department uses AI to estimate participants' emotions and determine the priority of interaction promotion. For example, if a participant is excited, the interaction promotion department will set a high priority for interaction promotion. If a participant is relaxed, it may set a low priority for interaction promotion. The interaction promotion department uses AI to analyze participants' emotional data and determine the optimal priority. This allows for appropriate interaction promotion by determining the priority of interaction promotion based on participants' emotions.

[0065] The Interaction Promotion Department selects the optimal interaction method when promoting interaction, taking into account the geographical location information of the participants. For example, the Interaction Promotion Department uses AI to consider the geographical location information of participants and select the optimal interaction method. For example, if a participant is in a specific area, the Interaction Promotion Department selects the optimal interaction method based on information related to that area. If a participant is on the move, the Interaction Promotion Department can also select the optimal interaction method based on information related to the area they are moving to. For example, the Interaction Promotion Department uses AI to analyze the geographical location information of participants and determine the optimal interaction method. This makes it possible to promote appropriate interaction by taking into account the geographical location information of participants.

[0066] The Interaction Promotion Department analyzes participants' social media activity and proposes ways to interact during the interaction promotion process. For example, the Interaction Promotion Department uses AI to analyze participants' social media activity and proposes ways to interact. For instance, if a participant mentions a specific artist on social media, the Interaction Promotion Department will propose ways to interact related to that artist. If a participant mentions a specific event on social media, the Interaction Promotion Department can also propose ways to interact related to that event. For example, the Interaction Promotion Department uses AI to analyze participants' social media activity and determine the optimal way to interact. This allows them to propose appropriate ways to interact by analyzing participants' social media activity.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The music festival experience enhancement robot system can also analyze participants' past behavioral data to suggest the most suitable live performances and activities. For example, if a participant has previously enjoyed performances by a particular artist, it can prioritize suggesting that artist's live performances. If a participant prefers a specific genre of music, it can suggest performances related to that genre. Furthermore, by analyzing past behavioral data, it can analyze when participants are most active and suggest activities best suited to those times. This allows for more personalized suggestions by leveraging participants' past behavioral data.

[0069] The robotic system for enhancing the music festival experience can also use participants' real-time location information to suggest the optimal route. For example, if a participant is heading to a specific stage, it can suggest the shortest route to that stage. If a participant wants to avoid crowds, it can also suggest a route that avoids crowds. Furthermore, it can use real-time location information to suggest activities and performances that participants might be interested in. In this way, it can utilize participants' location information to provide a more efficient and comfortable festival experience.

[0070] The music festival experience enhancement robot system can also analyze participants' social media activity and suggest the most suitable live performances and activities. For example, if a participant mentions a particular artist on social media, it can prioritize suggesting that artist's live performances. If a participant mentions a particular event on social media, it can also suggest activities related to that event. Furthermore, it can suggest new artists and events that participants might be interested in based on their social media activity. This allows for more personalized suggestions by leveraging participants' social media activity.

[0071] The robotic system for enhancing the music festival experience can also utilize real-time physiological data from participants to suggest optimal live performances and activities. For example, if a participant's heart rate is elevated, it can suggest an energetic performance. If a participant's heart rate is stable, it can suggest a relaxing activity. Furthermore, it can use real-time physiological data to monitor participants' health and suggest appropriate responses. This allows for more personalized suggestions based on participants' physiological data.

[0072] The music festival experience enhancement robot system can also analyze participants' past emotional data and suggest the most suitable live performances and activities. For example, if a participant has shown high emotional fluctuations during a particular artist's performance in the past, the system will prioritize suggesting that artist's live performances. If a participant is relaxed by a particular genre of music, the system can also suggest performances related to that genre. Furthermore, by analyzing past emotional data, the system can identify times when participants are prone to emotional fluctuations and suggest activities that are most suitable for those times. This allows for more personalized suggestions by leveraging participants' past emotional data.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of music festival participants. For example, the acquisition unit acquires participants' facial expression data using a camera. The acquisition unit can also acquire the tone of the participants' voices using a microphone. Furthermore, the acquisition unit can acquire the participants' heart rates using a sensor. For example, the acquisition unit analyzes the participants' facial expressions using facial recognition technology and acquires emotion estimation information. It can also analyze the tone of the participants' voices using a microphone and acquire emotion estimation information. It can also measure the participants' heart rates using a sensor and acquire emotion estimation information. Step 2: The emotion estimation unit estimates the participant's emotions in real time based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit analyzes the acquired information using, for example, AI, to estimate the participant's emotions. For example, the emotion estimation unit analyzes the participant's facial expressions using facial recognition technology to estimate emotions. It can also analyze the participant's voice tone using voice analysis technology to estimate emotions. It can also analyze fluctuations in heart rate to estimate emotions. Step 3: The suggestion unit proposes live performances and activities based on the emotions estimated by the emotion estimation unit. The suggestion unit, for example, uses AI to suggest the most suitable live performances and activities based on the participants' emotions. For example, if a participant is looking for energetic music, the suggestion unit will guide them to a lively performance. If a participant wants to relax, it can also suggest a calming activity. If a participant wants to make new friends, it can also suggest a social event.

[0075] (Example of form 2) The music festival experience enhancement robot system according to an embodiment of the present invention is a system that estimates participants' emotions in real time and proposes live performances and activities tailored to their preferences. This system acquires information for estimating participants' emotions and estimates their emotions in real time based on the acquired information. Based on the estimated emotions, it proposes live performances and activities. It also has a function to analyze emotion data and inform the audience of the venue's congestion status and waiting times in real time. Furthermore, it has a function to find other participants based on emotion data and promote interaction. For example, information for estimating participants' emotions can be obtained from the participant's facial expressions, tone of voice, heart rate, etc. For example, if a participant is smiling or speaking in an excited tone of voice, it is estimated that they have energetic emotions. This information is collected by the acquisition unit. Next, based on the acquired information, the system estimates the participant's emotions in real time. The emotion estimation unit analyzes the acquired information and estimates the participant's emotions. For example, if a participant is smiling, the emotion estimation unit estimates that the participant is feeling happy. This estimation includes AI processing. Based on the estimated emotions, it proposes live performances and activities. The suggestion unit proposes the most suitable live performances and activities for participants based on the emotions estimated by the emotion estimation unit. For example, if a participant is looking for energetic music, the suggestion unit will guide them to a lively performance. This suggestion also involves AI processing. Furthermore, it also has a function to analyze emotion data and inform participants of the venue's congestion level and waiting times in real time. The congestion notification unit informs participants of the venue's congestion level and waiting times in real time based on the emotions estimated by the emotion estimation unit. For example, if a participant wants to avoid crowds, the congestion notification unit will guide them to less crowded areas. This notification also involves AI processing. Finally, it also has a function to find other participants and facilitate interaction based on emotion data. The interaction facilitation unit finds other participants and facilitates interaction based on the emotions estimated by the emotion estimation unit. For example, if a participant wants to make new friends, the interaction facilitation unit will introduce them to other participants with similar emotions. This interaction also involves AI processing.This allows the music festival experience enhancement robot system to estimate participants' emotions in real time and suggest optimal live performances and activities.

[0076] The robot system for enhancing the music festival experience according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a suggestion unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of music festival participants. The acquisition unit acquires, for example, facial expression data of participants using a camera. The acquisition unit can also acquire the tone of voice of participants using a microphone. Furthermore, the acquisition unit can acquire the heart rate of participants using a sensor. For example, the acquisition unit analyzes the facial expressions of participants using facial recognition technology and acquires emotion estimation information. It can also analyze the tone of voice of participants using a microphone and acquire emotion estimation information. It can also measure the heart rate of participants using a sensor and acquire emotion estimation information. The emotion estimation unit estimates the emotions of participants in real time based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit analyzes the acquired information using, for example, AI and estimates the emotions of participants. For example, the emotion estimation unit analyzes the facial expressions of participants using facial recognition technology and estimates emotions. It can also analyze the tone of voice of participants using voice analysis technology and estimate emotions. The system can also analyze heart rate variability and estimate emotions. Based on the emotions estimated by the emotion estimation unit, the suggestion unit proposes live performances and activities. For example, the suggestion unit uses AI to suggest the most suitable live performances and activities based on the participant's emotions. For instance, if a participant is looking for energetic music, the suggestion unit will guide them to a lively performance. If a participant wants to relax, it can also suggest a calming activity. If a participant wants to make new friends, it can also suggest a social event. As a result, the music festival experience enhancement robot system according to this embodiment can estimate participants' emotions in real time and suggest the most suitable live performances and activities.

[0077] The acquisition unit acquires emotion estimation information, which is used to estimate the emotions of music festival participants. For example, the acquisition unit acquires participants' facial expression data using a camera. Specifically, the camera has high resolution and can capture subtle changes in facial expressions. This makes it possible to analyze participants' emotions such as joy, surprise, and sadness in detail. The acquisition unit can also acquire the tone of participants' voices using a microphone. The microphone is highly sensitive and can accurately capture changes in the volume and tone of participants' voices. This makes it possible to determine whether participants are excited or relaxed. Furthermore, the acquisition unit can acquire participants' heart rates using a sensor. Sensors come in various types, such as wristbands and chest straps, and can monitor heart rates in real time. For example, the acquisition unit analyzes participants' facial expressions using facial recognition technology and acquires emotion estimation information. The facial recognition technology employs a deep learning algorithm and can detect facial feature points with high accuracy. It can also analyze the tone of participants' voices using a microphone and acquire emotion estimation information. Voice analysis technology can analyze changes in the frequency components and volume of speech to capture changes in emotion. It can also measure participants' heart rates using sensors to acquire information for emotion estimation. Heart rate variability is an important indicator of a participant's level of excitement and stress, and analyzing this can improve the accuracy of emotion estimation. As a result, the acquisition unit can collect emotion estimation information from diverse sources, providing fundamental data for highly accurate estimation of participants' emotions.

[0078] The emotion estimation unit estimates the participant's emotions in real time based on emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit analyzes the acquired information using AI to estimate the participant's emotions. Specifically, the emotion estimation unit employs a neural network model using deep learning and comprehensively analyzes various data such as facial expressions, voice tone, and heart rate. For example, the emotion estimation unit analyzes the participant's facial expressions using facial recognition technology to estimate emotions. Facial recognition technology analyzes feature points such as the degree of eye opening, the degree of mouth corner upturning, and eyebrow movement, and can estimate emotions such as joy, surprise, sadness, and anger with high accuracy. It is also possible to analyze the participant's voice tone using voice analysis technology to estimate emotions. Voice analysis technology analyzes the frequency components, volume, and rhythm of the voice to estimate emotional states such as excitement, relaxation, and tension. It is also possible to analyze heart rate fluctuations to estimate emotions. Heart rate fluctuations are an important indicator of the degree of stress and excitement, and analyzing them can improve the accuracy of emotion estimation. Furthermore, the emotion estimation unit can improve the accuracy of emotion estimation by utilizing past data and statistical information. For example, it can learn patterns of emotional responses to specific music genres or performances based on data from past festival participants and reflect this in real-time emotion estimation. This allows the emotion estimation unit to quickly and accurately analyze acquired data and estimate participants' emotions in real time.

[0079] The suggestion unit proposes live performances and activities based on the emotions estimated by the emotion estimation unit. For example, the suggestion unit uses AI to suggest the most suitable live performances and activities based on the participant's emotions. Specifically, the suggestion unit analyzes the participant's emotional data and selects the performance or activity that best suits their current emotional state. For example, if a participant is looking for energetic music, the suggestion unit will recommend a lively performance. Energetic music often consists of fast-paced, rhythmically emphasized songs, which have the effect of increasing the participant's excitement. If a participant wants to relax, the suggestion unit can also suggest a calming activity. Relaxing music often consists of slow-paced, gently melodic songs, which have the effect of reducing the participant's stress. If a participant wants to make new friends, the suggestion unit can also suggest a social event. Social events provide an environment where participants can naturally start conversations with each other, helping them to build new relationships. Furthermore, the suggestion unit can make more personalized suggestions by considering the participant's past behavioral history and preferences. For example, it can make suggestions that best suit the current emotional state based on data of performances and activities that the participant has enjoyed participating in in the past. Furthermore, the proposal team can continuously revise its suggestions based on real-time updated sentiment data to provide the optimal experience. This allows the proposal team to suggest live performances and activities that are best suited to the participants' emotions, thereby enhancing the music festival experience.

[0080] The emotion estimation unit includes a congestion notification unit that informs participants of the congestion status or waiting time in the venue in real time based on the estimated emotion. The congestion notification unit uses AI, for example, to inform participants of the congestion status and waiting time in the venue in real time based on their emotions. For example, if a participant wants to avoid congestion, the congestion notification unit will guide them to a less crowded area. If a participant wants to reduce their waiting time, it can also suggest activities with shorter waiting times. The congestion notification unit uses AI, for example, to analyze the congestion status in the venue and notify participants in real time. This makes it possible to inform participants of the congestion status and waiting time in the venue in real time based on their emotions.

[0081] The emotion estimation unit includes an interaction facilitator that finds other participants and facilitates interaction based on the estimated emotions. The interaction facilitator, for example, uses AI to find other participants and facilitate interaction based on the participant's emotions. For example, if a participant feels like making new friends, the interaction facilitator will introduce them to other participants with similar emotions. If a participant has a particular interest, it can also find other participants with the same interest and facilitate interaction. The interaction facilitator, for example, uses AI to analyze the participant's emotional data and find the most suitable interaction partner. This allows it to find other participants and facilitate interaction based on the participant's emotions.

[0082] The acquisition unit acquires information for emotion estimation, such as the participant's facial expressions, voice tone, and heart rate. For example, the acquisition unit can acquire the participant's facial expressions using a camera. The acquisition unit can also acquire the participant's voice tone using a microphone. The acquisition unit can also acquire the participant's heart rate using a sensor. For example, the acquisition unit can analyze the participant's facial expressions using facial recognition technology and acquire information for emotion estimation. The acquisition unit can also analyze the participant's voice tone using voice analysis technology and acquire information for emotion estimation. The acquisition unit can also analyze fluctuations in heart rate and acquire information for emotion estimation. By acquiring information for emotion estimation, such as the participant's facial expressions, voice tone, and heart rate, the accuracy of emotion estimation is improved.

[0083] The suggestion unit proposes live performances and activities to participants based on the emotions estimated by the emotion estimation unit. For example, the suggestion unit uses AI to suggest the most suitable live performances and activities based on the participants' emotions. For instance, if a participant is looking for energetic music, the suggestion unit will recommend a lively performance. If a participant wants to relax, it can also suggest a calming activity. The suggestion unit analyzes the participants' emotional data using AI to make optimal suggestions. This allows it to propose the most suitable live performances and activities to participants based on the emotions estimated by the emotion estimation unit.

[0084] The data acquisition unit estimates the participant's emotions and adjusts the timing of acquiring emotion estimation information based on the estimated emotions. For example, the data acquisition unit uses AI to estimate the participant's emotions and adjusts the timing of acquiring emotion estimation information. For example, if the participant is excited, the data acquisition unit acquires emotion estimation information frequently to track changes in emotions in real time. If the participant is relaxed, the interval between acquiring emotion estimation information can be widened to save resources. For example, the data acquisition unit uses AI to analyze the participant's emotional data and determine the optimal acquisition timing. This allows for efficient use of resources by adjusting the timing of acquiring emotion estimation information based on the participant's emotions.

[0085] The data acquisition unit analyzes participants' past emotional data and selects the optimal acquisition method. For example, the data acquisition unit uses AI to analyze participants' past emotional data and select the optimal acquisition method. For example, if a participant has shown high emotional fluctuations during a particular event in the past, the data acquisition unit will prioritize acquiring information related to that event. It is also possible to analyze participants' past emotional data to identify periods when emotions tend to fluctuate and focus on acquiring information during those periods. For example, the data acquisition unit uses AI to analyze participants' past emotional data and determine the optimal acquisition method. This allows for the selection of the optimal acquisition method by analyzing participants' past emotional data, thereby improving the accuracy of emotion estimation.

[0086] The data acquisition unit filters the data for sentiment estimation based on the participant's current activities and areas of interest. For example, the data acquisition unit uses AI to filter the data based on the participant's current activities and areas of interest. For instance, if a participant is performing live, the data acquisition unit prioritizes acquiring information related to that performance. If a participant is participating in a specific activity, the data acquisition unit can also filter and acquire information related to that activity. For example, the data acquisition unit uses AI to analyze the participant's areas of interest and acquire the most relevant information. This allows for the acquisition of more relevant information by filtering the data based on the participant's current activities and areas of interest.

[0087] The data acquisition unit estimates the participant's emotions and determines the priority of emotion estimation information to acquire based on the estimated emotions. For example, the data acquisition unit uses AI to estimate the participant's emotions and determines the priority of emotion estimation information. For example, if the participant is excited, the data acquisition unit prioritizes acquiring physiological data such as heart rate and voice tone. If the participant is relaxed, it may also prioritize acquiring facial expression data. For example, the data acquisition unit uses AI to analyze the participant's emotional data and determine the optimal priority. This allows for the priority acquisition of important information by determining the priority of information based on the participant's emotions.

[0088] The data acquisition unit prioritizes acquiring highly relevant information by considering the participant's geographical location when acquiring information for sentiment estimation. For example, the data acquisition unit uses AI to consider the participant's geographical location when acquiring information for sentiment estimation. For example, if the participant is approaching a specific stage, the data acquisition unit prioritizes acquiring information related to that stage. If the participant is staying in a specific area, it can also prioritize acquiring information related to that area. For example, the data acquisition unit uses AI to analyze the participant's geographical location and acquire the most relevant information. This allows for the priority acquisition of highly relevant information by considering the participant's geographical location.

[0089] The data acquisition unit analyzes participants' social media activity and acquires relevant information when acquiring information for sentiment estimation. For example, the data acquisition unit uses AI to analyze participants' social media activity and acquire information for sentiment estimation. For example, if a participant mentions a specific artist on social media, the data acquisition unit prioritizes acquiring information related to that artist. If a participant mentions a specific event on social media, it can also prioritize acquiring information related to that event. For example, the data acquisition unit uses AI to analyze participants' social media activity and acquire the most relevant information. This allows for the priority acquisition of relevant information by analyzing participants' social media activity.

[0090] The emotion estimation unit estimates the participant's emotions and adjusts the emotion estimation algorithm based on the estimated emotions. For example, the emotion estimation unit uses AI to estimate the participant's emotions and adjusts the emotion estimation algorithm. For instance, if the participant is excited, the emotion estimation unit speeds up the emotion estimation algorithm to track emotional changes in real time. If the participant is relaxed, it can also slow down the emotion estimation algorithm to save resources. The emotion estimation unit uses AI to analyze the participant's emotional data and adjust the optimal algorithm. This allows for real-time tracking of emotional changes by adjusting the emotion estimation algorithm based on the participant's emotions.

[0091] The emotion estimation unit improves estimation accuracy by referring to the participant's past emotional data during emotion estimation. For example, the emotion estimation unit uses AI to refer to the participant's past emotional data and improve the accuracy of emotion estimation. For example, if the emotion estimation unit finds that a participant has shown high emotional fluctuations in a particular event in the past, it refers to that data to improve estimation accuracy. It can also analyze the participant's past emotional data to identify periods when emotions tend to fluctuate and refer to that data to improve estimation accuracy. For example, the emotion estimation unit uses AI to analyze the participant's past emotional data and select the optimal estimation method. This allows for improved accuracy of emotion estimation by referring to the participant's past emotional data.

[0092] The emotion estimation unit considers the participant's attribute information when estimating emotions. For example, the emotion estimation unit uses AI to consider the participant's attribute information and perform emotion estimation. For example, the emotion estimation unit adjusts the emotion estimation algorithm based on the participant's age to improve estimation accuracy. It can also adjust the emotion estimation algorithm based on the participant's gender to improve estimation accuracy. For example, the emotion estimation unit uses AI to analyze the participant's attribute information and select the optimal estimation method. This improves the accuracy of emotion estimation by considering the participant's attribute information.

[0093] The emotion estimation unit estimates the participant's emotions and adjusts the display method of the emotion estimation results based on the estimated emotions. For example, the emotion estimation unit uses AI to estimate the participant's emotions and adjusts the display method of the emotion estimation results. For example, if the participant is excited, the emotion estimation unit can provide a visually stimulating display method. If the participant is relaxed, it can also provide a calm display method. For example, the emotion estimation unit uses AI to analyze the participant's emotional data and determine the optimal display method. This allows for a visually appropriate display by adjusting the display method based on the participant's emotions.

[0094] The sentiment estimation unit considers the geographical distribution of participants when estimating sentiment. For example, the sentiment estimation unit uses AI to consider the geographical distribution of participants and perform sentiment estimation. For example, if participants are concentrated in a specific area, the sentiment estimation unit performs sentiment estimation based on information related to that area. If participants are widely dispersed, it can also perform sentiment estimation by integrating information from each area. For example, the sentiment estimation unit uses AI to analyze the geographical distribution of participants and select the optimal estimation method. This improves the accuracy of sentiment estimation by considering the geographical distribution of participants.

[0095] The sentiment estimation unit improves its estimation accuracy by referring to relevant literature and data during sentiment estimation. For example, the sentiment estimation unit uses AI to refer to relevant literature and data to improve the accuracy of sentiment estimation. For example, the sentiment estimation unit refers to the latest research papers on sentiment estimation and improves its algorithm. It can also improve the accuracy of sentiment estimation by referring to past event data. For example, the sentiment estimation unit uses AI to analyze the sentiment data of other participants and select the optimal estimation method. In this way, the accuracy of sentiment estimation can be improved by referring to relevant literature and data.

[0096] The proposal department estimates the participants' emotions and adjusts the presentation of the proposal based on those estimated emotions. For example, the proposal department might use AI to estimate the participants' emotions and adjust the presentation of the proposal. For instance, if a participant is excited, the proposal department might present a visually stimulating proposal. If a participant is relaxed, it might present a calmer proposal. The proposal department might use AI to analyze the participants' emotional data and determine the optimal presentation method. This allows for visually appropriate proposals by adjusting the presentation method based on the participants' emotions.

[0097] The proposal team adjusts the level of detail in their proposals based on the importance of the live performances and activities. For example, they might use AI to adjust the level of detail based on the importance of the live performances and activities. For instance, they might provide detailed information for important live performances, or concise information for less important activities. The proposal team might use AI to analyze participants' levels of interest and determine the optimal level of detail for each proposal. This allows for the provision of appropriate information by adjusting the level of detail based on the importance of the live performances and activities.

[0098] The proposal team applies different proposal algorithms depending on the category of the live performance or activity. For example, the proposal team uses AI to apply proposal algorithms according to the category of the live performance or activity. For instance, in the case of a music performance, the proposal team makes suggestions based on the music genre. In the case of an art exhibition, it can also make suggestions based on the exhibition content. For example, the proposal team uses AI to analyze the participants' areas of interest and select the optimal proposal algorithm. By applying different proposal algorithms depending on the category of the live performance or activity, more appropriate suggestions become possible.

[0099] The proposal team estimates the participant's emotions and adjusts the length of the proposal based on the estimated emotions. For example, the proposal team might use AI to estimate the participant's emotions and adjust the proposal length. For instance, if the participant is excited, the proposal team might provide a short, concise proposal. If the participant is relaxed, it might provide a longer proposal with more detailed explanations. The proposal team might use AI to analyze the participant's emotional data and determine the optimal proposal length. This allows for the provision of appropriate information by adjusting the proposal length based on the participant's emotions.

[0100] The proposal team prioritizes proposals based on the timing of live performances and activities. For example, they might use AI to prioritize proposals based on the timing of live performances and activities. For instance, they might prioritize proposing upcoming live performances. They might also prioritize proposing important activities. The proposal team might use AI to analyze participants' schedules and determine the optimal proposal priority. This allows for the provision of appropriate information by prioritizing proposals based on the timing of live performances and activities.

[0101] The suggestion department adjusts the order of suggestions based on the relevance of live performances and activities. For example, it uses AI to adjust the order of suggestions based on the relevance of live performances and activities. For example, the suggestion department prioritizes suggesting highly relevant performances based on participants' interests. It can also suggest highly relevant activities based on participants' past participation history. For example, the suggestion department uses AI to analyze participants' emotional data and determine the optimal suggestion order. This allows for the provision of appropriate information by adjusting the order of suggestions based on the relevance of live performances and activities.

[0102] The congestion notification unit estimates participants' emotions and adjusts the congestion notification method based on the estimated emotions. For example, the congestion notification unit uses AI to estimate participants' emotions and adjust the congestion notification method. For example, if a participant is excited, the congestion notification unit can provide a visually stimulating notification method. If a participant is relaxed, it can also provide a calm notification method. For example, the congestion notification unit uses AI to analyze participants' emotional data and determine the optimal notification method. This makes it possible to provide appropriate information by adjusting the congestion notification method based on participants' emotions.

[0103] The congestion notification unit predicts the current congestion level by referring to past congestion data when issuing congestion notifications. For example, the congestion notification unit uses AI to refer to past congestion data and predict the current congestion level. For example, the congestion notification unit predicts congestion levels for specific time periods based on past congestion data. It can also analyze congestion patterns in specific areas from past congestion data and predict the current congestion level. For example, the congestion notification unit uses AI to analyze past congestion data and select the optimal prediction method. This allows for the prediction of current congestion levels by referring to past congestion data, enabling the provision of appropriate information.

[0104] The congestion notification unit applies different notification methods to each area within the venue when notifying about congestion. For example, the congestion notification unit uses AI to apply different notification methods to each area within the venue. For example, if a particular area is congested, the congestion notification unit will prioritize notifying information related to that area. If a particular area is not crowded, it can also prioritize notifying information related to that area. For example, the congestion notification unit uses AI to analyze the congestion status of each area within the venue and select the optimal notification method. This makes it possible to provide appropriate information by applying different notification methods to each area within the venue.

[0105] The congestion notification unit estimates participants' emotions and adjusts the importance of congestion status based on the estimated emotions. For example, the congestion notification unit uses AI to estimate participants' emotions and adjust the importance of congestion status. For example, if a participant is excited, the congestion notification unit will prioritize notifying them of congestion status. If a participant is relaxed, it may also reduce the frequency of congestion status notifications. For example, the congestion notification unit uses AI to analyze participants' emotional data and determine the optimal importance. This allows for the provision of appropriate information by adjusting the importance of congestion status based on participants' emotions.

[0106] The congestion notification unit considers the geographical information of the area within the venue when issuing congestion notifications. For example, the congestion notification unit uses AI to consider the geographical information of the area within the venue when issuing congestion notifications. For example, if a particular area is congested, the congestion notification unit will issue a notification based on the geographical information of that area. It can also issue a notification based on the geographical information of a particular area if it is not crowded. For example, the congestion notification unit uses AI to analyze the geographical information of each area within the venue and select the optimal notification method. This makes it possible to provide appropriate information by considering the geographical information of the area within the venue.

[0107] The congestion notification unit improves notification accuracy by referring to relevant data when issuing congestion notifications. For example, the congestion notification unit uses AI to refer to relevant data and improve notification accuracy. For example, the congestion notification unit refers to past congestion data to improve notification accuracy. It can also refer to the sentiment data of other participants to improve notification accuracy. For example, the congestion notification unit uses AI to analyze real-time congestion data and select the optimal notification method. This improves notification accuracy and enables the provision of appropriate information by referring to relevant data.

[0108] The interaction facilitator estimates participants' emotions and adjusts interaction facilitator methods based on these estimates. For example, the interaction facilitator might use AI to estimate participants' emotions and adjust the methods accordingly. If a participant is excited, the interaction facilitator might provide visually stimulating interaction facilitator methods. If a participant is relaxed, it might provide calming interaction facilitator methods. The interaction facilitator might use AI to analyze participants' emotional data and determine the optimal interaction facilitator method. This allows for appropriate interaction facilitator methods to be adjusted based on participants' emotions.

[0109] The interaction promotion department selects the optimal interaction method by referring to participants' past interaction history when promoting interaction. For example, the interaction promotion department may use AI to refer to participants' past interaction history and select the optimal interaction method. For example, the interaction promotion department may select the optimal interaction method based on participants' past interaction history at specific events. It is also possible to analyze from participants' past interaction history whether interaction is active at certain times and focus interaction promotion on those times. For example, the interaction promotion department may use AI to analyze participants' past interaction history and select the optimal interaction method. This makes it possible to select the optimal interaction method and promote appropriate interaction by referring to participants' past interaction history.

[0110] The interaction promotion department considers participants' attribute information when facilitating interaction. For example, the department uses AI to consider participants' attribute information when facilitating interaction. For example, the department selects the optimal interaction method based on the participant's age. It can also select the optimal interaction method based on the participant's gender. For example, the department uses AI to analyze participants' attribute information and determine the optimal interaction method. This makes it possible to promote appropriate interaction by considering participants' attribute information.

[0111] The interaction promotion department estimates participants' emotions and determines the priority of interaction promotion based on those estimated emotions. For example, the interaction promotion department uses AI to estimate participants' emotions and determine the priority of interaction promotion. For example, if a participant is excited, the interaction promotion department will set a high priority for interaction promotion. If a participant is relaxed, it may set a low priority for interaction promotion. The interaction promotion department uses AI to analyze participants' emotional data and determine the optimal priority. This allows for appropriate interaction promotion by determining the priority of interaction promotion based on participants' emotions.

[0112] The Interaction Promotion Department selects the optimal interaction method when promoting interaction, taking into account the geographical location information of the participants. For example, the Interaction Promotion Department uses AI to consider the geographical location information of participants and select the optimal interaction method. For example, if a participant is in a specific area, the Interaction Promotion Department selects the optimal interaction method based on information related to that area. If a participant is on the move, the Interaction Promotion Department can also select the optimal interaction method based on information related to the area they are moving to. For example, the Interaction Promotion Department uses AI to analyze the geographical location information of participants and determine the optimal interaction method. This makes it possible to promote appropriate interaction by taking into account the geographical location information of participants.

[0113] The Interaction Promotion Department analyzes participants' social media activity and proposes ways to interact during the interaction promotion process. For example, the Interaction Promotion Department uses AI to analyze participants' social media activity and proposes ways to interact. For instance, if a participant mentions a specific artist on social media, the Interaction Promotion Department will propose ways to interact related to that artist. If a participant mentions a specific event on social media, the Interaction Promotion Department can also propose ways to interact related to that event. For example, the Interaction Promotion Department uses AI to analyze participants' social media activity and determine the optimal way to interact. This allows them to propose appropriate ways to interact by analyzing participants' social media activity.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The robot system for enhancing the music festival experience can also include a health management unit that estimates participants' emotions and monitors their health based on those emotions. For example, if a participant is feeling tired, the health management unit can suggest a break. If a participant is excited, the health management unit can also encourage hydration. Furthermore, the health management unit can monitor participants' heart rate and body temperature and suggest appropriate actions if abnormalities are detected. This allows for real-time monitoring of participants' health and the suggestion of appropriate actions.

[0116] The robot system for enhancing the music festival experience can also include a food and beverage suggestion unit that estimates participants' emotions and, based on those emotions, suggests food and beverages tailored to their preferences. For example, if a participant feels like relaxing, the food and beverage suggestion unit might suggest a cafe or light meal. If a participant is feeling energetic, the unit might suggest an energy drink or a high-calorie meal. Furthermore, the food and beverage suggestion unit can also consider participants' allergy information and dietary restrictions when suggesting the most suitable food and beverages. This allows for the suggestion of optimal food and beverages based on participants' emotions and health conditions.

[0117] The robotic system for enhancing the music festival experience can also include a stress management unit that estimates participants' emotions and manages their stress levels based on those estimates. For example, if a participant is experiencing high stress, the stress management unit can suggest relaxation areas or massage services. If a participant is relaxed, the stress management unit can also suggest activities to maintain that state. Furthermore, the stress management unit can monitor participants' heart rate and breathing patterns to assess their stress levels in real time. This allows for the management of participants' stress levels and the suggestion of appropriate responses.

[0118] The robotic system for enhancing the music festival experience can also include a safety management unit that estimates participants' emotions and ensures their safety based on those emotions. For example, if a participant is feeling anxious, the safety management unit can suggest moving to a safe area. If a participant is excited, the safety management unit can also suggest a route to avoid crowds. Furthermore, the safety management unit can monitor participants' location information and respond quickly in emergencies. This ensures the safety of participants, allowing them to enjoy the festival with peace of mind.

[0119] The robotic system for enhancing the music festival experience can also include an energy management unit that estimates participants' emotions and manages their energy levels based on those emotions. For example, if a participant is feeling tired, the energy management unit can suggest a break or a snack. If a participant is feeling energetic, the energy management unit can also suggest active activities. Furthermore, the energy management unit can monitor participants' heart rate and activity levels and assess their energy levels in real time. This allows the system to manage participants' energy levels and suggest appropriate responses.

[0120] The music festival experience enhancement robot system can also analyze participants' past behavioral data to suggest the most suitable live performances and activities. For example, if a participant has previously enjoyed performances by a particular artist, it can prioritize suggesting that artist's live performances. If a participant prefers a specific genre of music, it can suggest performances related to that genre. Furthermore, by analyzing past behavioral data, it can analyze when participants are most active and suggest activities best suited to those times. This allows for more personalized suggestions by leveraging participants' past behavioral data.

[0121] The robotic system for enhancing the music festival experience can also use participants' real-time location information to suggest the optimal route. For example, if a participant is heading to a specific stage, it can suggest the shortest route to that stage. If a participant wants to avoid crowds, it can also suggest a route that avoids crowds. Furthermore, it can use real-time location information to suggest activities and performances that participants might be interested in. In this way, it can utilize participants' location information to provide a more efficient and comfortable festival experience.

[0122] The music festival experience enhancement robot system can also analyze participants' social media activity and suggest the most suitable live performances and activities. For example, if a participant mentions a particular artist on social media, it can prioritize suggesting that artist's live performances. If a participant mentions a particular event on social media, it can also suggest activities related to that event. Furthermore, it can suggest new artists and events that participants might be interested in based on their social media activity. This allows for more personalized suggestions by leveraging participants' social media activity.

[0123] The robotic system for enhancing the music festival experience can also utilize real-time physiological data from participants to suggest optimal live performances and activities. For example, if a participant's heart rate is elevated, it can suggest an energetic performance. If a participant's heart rate is stable, it can suggest a relaxing activity. Furthermore, it can use real-time physiological data to monitor participants' health and suggest appropriate responses. This allows for more personalized suggestions based on participants' physiological data.

[0124] The music festival experience enhancement robot system can also analyze participants' past emotional data and suggest the most suitable live performances and activities. For example, if a participant has shown high emotional fluctuations during a particular artist's performance in the past, the system will prioritize suggesting that artist's live performances. If a participant is relaxed by a particular genre of music, the system can also suggest performances related to that genre. Furthermore, by analyzing past emotional data, the system can identify times when participants are prone to emotional fluctuations and suggest activities that are most suitable for those times. This allows for more personalized suggestions by leveraging participants' past emotional data.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of music festival participants. For example, the acquisition unit acquires participants' facial expression data using a camera. The acquisition unit can also acquire the tone of the participants' voices using a microphone. Furthermore, the acquisition unit can acquire the participants' heart rates using a sensor. For example, the acquisition unit analyzes the participants' facial expressions using facial recognition technology and acquires emotion estimation information. It can also analyze the tone of the participants' voices using a microphone and acquire emotion estimation information. It can also measure the participants' heart rates using a sensor and acquire emotion estimation information. Step 2: The emotion estimation unit estimates the participant's emotions in real time based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit analyzes the acquired information using, for example, AI, to estimate the participant's emotions. For example, the emotion estimation unit analyzes the participant's facial expressions using facial recognition technology to estimate emotions. It can also analyze the participant's voice tone using voice analysis technology to estimate emotions. It can also analyze fluctuations in heart rate to estimate emotions. Step 3: The suggestion unit proposes live performances and activities based on the emotions estimated by the emotion estimation unit. The suggestion unit, for example, uses AI to suggest the most suitable live performances and activities based on the participants' emotions. For example, if a participant is looking for energetic music, the suggestion unit will guide them to a lively performance. If a participant wants to relax, it can also suggest a calming activity. If a participant wants to make new friends, it can also suggest a social event.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] For example, the acquisition unit uses the camera 42 and microphone 38B of the smart device 14 to acquire the participant's facial expressions and voice tone, which are then analyzed by the identification processing unit 290 of the data processing device 12. The emotion estimation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and estimates the participant's emotions in real time based on the information from the acquisition unit. The suggestion unit is implemented, for example, by the control unit 46A of the smart device 14, and proposes live performances and activities based on the emotions estimated by the emotion estimation unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] For example, the acquisition unit uses the camera 42 and microphone 238 of the smart glasses 214 to acquire the participant's facial expressions and voice tone, which are then analyzed by the identification processing unit 290 of the data processing device 12. The emotion estimation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and estimates the participant's emotions in real time based on the information from the acquisition unit. The suggestion unit is implemented, for example, by the control unit 46A of the smart glasses 214, and suggests live performances and activities based on the emotions estimated by the emotion estimation unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] For example, the acquisition unit uses the camera 42 and microphone 238 of the headset terminal 314 to acquire the participant's facial expressions and voice tone, which are then analyzed by the identification processing unit 290 of the data processing device 12. The emotion estimation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and estimates the participant's emotions in real time based on the information from the acquisition unit. The suggestion unit is implemented, for example, by the control unit 46A of the headset terminal 314, and proposes live performances and activities based on the emotions estimated by the emotion estimation unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0172] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] For example, the acquisition unit uses the camera 42 and microphone 238 of the robot 414 to acquire the participant's facial expressions and voice tone, which are then analyzed by the identification processing unit 290 of the data processing device 12. The emotion estimation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and estimates the participant's emotions in real time based on the information from the acquisition unit. The suggestion unit is implemented, for example, by the control unit 46A of the robot 414, and proposes live performances and activities based on the emotions estimated by the emotion estimation unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0180] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0182] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0183] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0184] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0189] 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.

[0190] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0191] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0192] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0193] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0197] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0198] (Note 1) An acquisition unit that acquires emotion estimation information, which is information used to estimate the emotions of participants in a music festival, An emotion estimation unit estimates the emotions of the participants in real time based on the emotion estimation information acquired by the acquisition unit, The system includes a suggestion unit that proposes live performances and activities based on the emotions estimated by the emotion estimation unit. A system characterized by the following features. (Note 2) The venue is equipped with a congestion status notification unit that provides real-time information on congestion levels or waiting times based on the emotions estimated by the emotion estimation unit. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system includes an interaction facilitator that finds other participants and facilitates interaction based on the emotions estimated by the emotion estimation unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, We obtain information for emotion estimation from participants' facial expressions, tone of voice, and heart rate. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the emotions estimated by the emotion estimation unit, live performances and activities are suggested to participants. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the participants' emotions and adjusts the timing of acquiring emotion estimation information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Analyze participants' past emotional data and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring information for sentiment estimation, filtering is performed based on the participant's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, The system estimates the emotions of the participants and determines the priority of emotion estimation information to be acquired based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring information for sentiment estimation, the system prioritizes acquiring highly relevant information by considering the participants' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring information for sentiment estimation, the social media activity of participants is analyzed to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The emotion estimation unit, The system estimates the participants' emotions and adjusts the emotion estimation algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The emotion estimation unit, When estimating emotions, we improve estimation accuracy by referencing participants' past emotional data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The emotion estimation unit, When estimating emotions, the participant's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The emotion estimation unit, The system estimates the emotions of the participants and adjusts how the emotion estimation results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The emotion estimation unit, When estimating emotions, the geographical distribution of participants is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The emotion estimation unit, When estimating emotions, we refer to relevant literature and data to improve estimation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the participants' emotions and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the live performance or activity. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the live performance or activity. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, The system estimates the participants' emotions and adjusts the length of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of live performances and activities. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of live performances and activities. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned congestion status notification unit, The system estimates participants' emotions and adjusts how congestion status is notified based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned congestion status notification unit, When a congestion status notification is sent, past congestion data is used to predict the current congestion status. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned congestion status notification unit, When notifying about congestion levels, different notification methods will be applied to each area within the venue. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned congestion status notification unit, The system estimates participants' emotions and adjusts the importance of congestion levels based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned congestion status notification unit, When notifying about congestion levels, the notification will take into account the geographical information of the area within the venue. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned congestion status notification unit, When notifying about congestion levels, we refer to relevant data to improve notification accuracy. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned Exchange Promotion Department, The system estimates the participants' emotions and adjusts interaction facilitation methods based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned Exchange Promotion Department, When promoting interaction, the most suitable method of interaction is selected by referring to the participants' past interaction history. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned Exchange Promotion Department, When facilitating interaction, consider the participants' attribute information to facilitate communication. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned Exchange Promotion Department, The system estimates the emotions of the participants and determines the priority of interaction promotion based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned Exchange Promotion Department, When promoting interaction, the most suitable method of interaction is selected by considering the geographical location information of the participants. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned Exchange Promotion Department, When promoting interaction, we analyze participants' social media activity and propose ways to facilitate interaction. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires emotion estimation information, which is information used to estimate the emotions of participants in a music festival, An emotion estimation unit estimates the emotions of the participants in real time based on the emotion estimation information acquired by the acquisition unit, The system includes a suggestion unit that proposes live performances and activities based on the emotions estimated by the emotion estimation unit. A system characterized by the following features.

2. The venue is equipped with a congestion status notification unit that provides real-time information on congestion levels or waiting times based on the emotions estimated by the emotion estimation unit. The system according to feature 1.

3. The system includes an interaction facilitator that finds other participants and facilitates interaction based on the emotions estimated by the emotion estimation unit. The system according to feature 1.

4. The acquisition unit is, The participant's facial expression, tone of voice, and heart rate are acquired as information for emotion estimation. The system according to feature 1.

5. The acquisition unit is, The past emotional data of the aforementioned participants will be analyzed, and the optimal acquisition method will be selected. The system according to feature 1.

6. The acquisition unit is, When acquiring the aforementioned emotion estimation information, filtering is performed based on the participant's current activities and areas of interest. The system according to feature 1.

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