system

Abstract

The system according to this embodiment aims to propose an efficient and optimal method and provide necessary information for preparing for school plays and performances at kindergartens and daycare centers. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an information provision unit. The data collection unit collects past event data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal method based on the analysis results obtained by the analysis unit. The information provision unit provides necessary information based on the method proposed by the proposal 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, the method 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 conventional technology, it is difficult to efficiently find an optimal method in preparing for play parties in kindergartens or nurseries, and there is a problem that the burden on the kindergartens or guardians is large.

[0005] The system according to the embodiment aims to efficiently propose an optimal method and provide necessary information in preparing for play parties in kindergartens or nurseries.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an information provision unit. The data collection unit collects past event data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal method based on the analysis results obtained by the analysis unit. The information provision unit provides the necessary information based on the method proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently suggest the optimal method and provide the necessary information for preparing for a play at a kindergarten or nursery school. [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 play preparation support system according to an embodiment of the present invention is a system that supports the preparation of play events held regularly at kindergartens and nursery schools. This play preparation support system provides support for efficiently handling many matters that need to be considered, such as selecting a play, choreography, casting, practice methods, procurement of materials, and communication. The play preparation support system collects past event data and analyzes it using AI to propose the optimal method and provide necessary information. This reduces the burden on the school and parents and allows preparations to proceed efficiently. For example, by collecting past event data and analyzing it with AI, the play preparation support system can understand which plays are popular, which choreography is easy for children to remember, and which casting is effective. Based on this, it proposes the optimal play, choreography, and casting. It also proposes the optimal method for practice methods, procurement of materials, and communication based on past data. Furthermore, based on the proposed method, it links various information networks and provides necessary information. For example, it centrally manages practice schedules, material suppliers, and contact methods and provides them to the school and parents. This allows preparations to proceed efficiently and reduces the burden. In this way, the Play Day Preparation Support System is a system designed to support the preparation of play days at kindergartens and daycare centers, reducing the burden on the school and parents, and allowing them to enjoy the process leading up to the play day. This allows the Play Day Preparation Support System to reduce the burden on the school and parents and to proceed with preparations efficiently.

[0029] The school play preparation support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an information provision unit. The data collection unit collects past event data. The data collection unit can collect data such as the type of past event, the duration, and the number of participants. The data collection unit can also automatically collect past event data using AI. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, determine which plays are popular, which choreography is easy for children to remember, and which casting is effective based on the collected data. The analysis unit can also analyze the collected data using AI. The proposal unit proposes the optimal method based on the analysis results obtained by the analysis unit. The proposal unit proposes methods such as the optimal play, choreography, casting, practice methods, material procurement, and communication. The proposal unit can also propose the optimal method based on the analysis results using AI. The information provision unit provides the necessary information based on the method proposed by the proposal unit. The information provision department centrally manages and provides information such as practice schedules, material suppliers, and contact methods to the kindergarten and parents. The information provision department can also use AI to provide necessary information based on suggested methods. As a result, the play preparation support system according to this embodiment reduces the burden on the kindergarten and parents and allows preparations to proceed efficiently.

[0030] The data collection unit collects historical event data. For example, it can collect data such as the type of past event, its duration, and the number of participants. Specifically, it collects details such as the program content of past school plays, the types of performances, the duration of each performance, the ages and number of participating children, parental involvement, and details of costumes and props used. This data is crucial for understanding the success factors and areas for improvement of events. The data collection unit can also use AI to automatically collect historical event data. For example, it can extract and organize necessary information from databases and cloud storage where records of past events are stored. AI can use natural language processing to extract useful information from text data and image recognition to analyze details of performances and choreography from photos and videos. This allows the data collection unit to collect information more efficiently and accurately than manual data collection. Furthermore, the data collection unit can collect relevant data from publicly available information on the internet and social media posts. This allows for the collection of broader information and improved analysis accuracy. In addition, the data collection unit centrally manages the collected data, making it easily accessible to the analysis and proposal units. This will improve the overall efficiency and interoperability of the system.

[0031] The analysis department analyzes the data collected by the data collection department. For example, based on the collected data, the analysis department can determine which performances are popular, which choreography is easy for children to remember, and which casting is effective. Specifically, it statistically analyzes past event data to identify patterns in popular performances and choreography. For example, it can extract the characteristics of performances that are popular with children of a specific age group and choreography that is easy to remember. Regarding casting, it can propose the optimal division of roles based on the children's personalities, special skills, and past performance data. The analysis department can also use AI to analyze the collected data. The AI ​​uses machine learning algorithms to learn patterns and trends from the data and make predictions for future events. For example, based on past data, it can predict which performances are most likely to be successful at the next school play. The AI ​​can also detect anomalies in the data, identify areas where problems occurred in past events, and suggest areas for improvement. In this way, the analysis department can support the preparation of more effective and successful school plays based on the collected data. Furthermore, the analysis department visualizes the data and provides information in a way that is easy for kindergartens and parents to understand. For example, graphs and charts can be used to visually show trends in popular performances and choreography. This allows the analysis department to support data-driven decision-making and promote efficient preparation.

[0032] The proposal department proposes the optimal method based on the analysis results obtained by the analysis department. For example, the proposal department proposes methods for optimal performances and choreography, casting, practice methods, material procurement, and communication. Specifically, based on the analysis results, they select performances and choreography that children can enjoy and easily learn, and propose practice schedules and methods. For example, they propose performances suitable for specific age groups, setting practice times to maintain children's concentration, and effective practice methods. Regarding casting, they propose the optimal division of roles considering the children's special skills and personalities. The proposal department can also use AI to propose the optimal method based on the analysis results. The AI ​​automatically proposes the optimal combination of performances, choreography, and casting based on past data and analysis results. Furthermore, the AI ​​can monitor the progress of practice and propose adjustments to practice methods and schedules as needed. In this way, the proposal department can support efficient and effective preparation and help realize a highly successful school play. In addition, the proposal department also makes proposals regarding material procurement and communication. For example, they propose sources and costs for necessary costumes and props, as well as methods for communication and scheduling. This allows the proposal team to improve the overall efficiency of preparations and reduce the burden on the school and parents.

[0033] The Information Provision Department provides necessary information based on the methods proposed by the Proposal Department. For example, the Information Provision Department centrally manages and provides information such as practice schedules, material suppliers, and contact methods to the school and parents. Specifically, based on the proposed practice schedule, it creates practice plans for each class and group and shares them with parents and other relevant parties. It also creates a list of necessary materials and provides information such as suppliers, costs, and delivery dates. Furthermore, regarding contact methods, it provides means to communicate practice progress and important announcements in a timely manner. The Information Provision Department can also use AI to provide necessary information based on the proposed methods. The AI ​​monitors practice progress in real time and automatically notifies if schedule changes or additional practice are needed. The AI ​​also manages material inventory and supplier information and can automatically order necessary materials before they run out. This allows the Information Provision Department to provide information efficiently and accurately, and to ensure smooth progress in preparations. Furthermore, the Information Provision Department can collect feedback from parents and other relevant parties and continuously improve the proposed content and methods of information provision. For example, it can collect opinions on practice schedules and contact methods and incorporate them into future events. This allows the information department to provide appropriate support based on the latest information at all times, thereby reducing the burden on kindergartens and parents.

[0034] The data collection unit can collect historical event data. For example, it can collect data such as the type of past event, its duration, and the number of participants. The data collection unit can also use AI to automatically collect historical event data. This allows for the provision of data necessary for analysis. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to automatically collect data from publicly available databases on the internet in order to collect historical event data.

[0035] The analysis unit can analyze the collected data to understand which performances are popular, which choreography is easy for children to remember, and which casting is effective. For example, the analysis unit can identify popular performances, easy-to-remember choreography, and effective casting based on the collected data. The analysis unit can also use AI to analyze the collected data. This allows the analysis unit to identify popular performances, easy-to-remember choreography, and effective casting by analyzing the collected data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into the AI, which will analyze the data to identify popular performances, easy-to-remember choreography, and effective casting.

[0036] The proposal department can propose optimal performances, choreography, casting, practice methods, material procurement, and communication methods based on the analysis results. For example, the proposal department can propose optimal performances, choreography, casting, practice methods, material procurement, and communication methods based on the analysis results. The proposal department can also use AI to propose optimal methods based on the analysis results. This allows for efficient preparation by proposing optimal methods based on the analysis results. Some or all of the above-mentioned processes in the proposal department may be performed using AI or not. For example, the proposal department inputs the analysis results into AI, and the AI ​​proposes optimal performances, choreography, casting, practice methods, material procurement, and communication methods.

[0037] The Information Provision Department can centrally manage and provide practice schedules, material suppliers, contact methods, etc., to the kindergarten and parents based on the proposed method. The Information Provision Department can also use AI to provide the necessary information based on the proposed method. This allows for efficient preparation by providing the necessary information based on the proposed method. Some or all of the above processing in the Information Provision Department may be performed using AI or not. For example, the Information Provision Department inputs practice schedules, material suppliers, contact methods, etc., into the AI ​​based on the proposed method, and the AI ​​centrally manages and provides this information to the kindergarten and parents.

[0038] The data collection unit can select data from past events by considering factors such as the success of the event and participant satisfaction. For example, the data collection unit can prioritize collecting data from events with high success rates and analyze the factors contributing to that success. The data collection unit can also collect data from events with high participant satisfaction and identify the factors contributing to that satisfaction. Furthermore, the data collection unit can collect data from events with low success rates and low satisfaction rates to identify areas for improvement. By selecting data based on factors such as event success and participant satisfaction, more useful data can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input event success rates and participant satisfaction into an AI, which then selects the data.

[0039] The data collection unit can filter data based on the scale of the event and the age range of the participants during data collection. For example, the data collection unit can prioritize collecting data from large-scale events and find the optimal method for that scale. The data collection unit can also collect data based on the age range of the participants and identify methods suitable for that age group. The data collection unit can also collect data from small-scale events and find areas for improvement based on the scale. In this way, appropriate data can be collected by filtering data based on the scale of the event and the age range of the participants. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the scale of the event and the age range of the participants into the AI, and the AI ​​filters the data.

[0040] The data collection unit can collect data while considering the characteristics of events in each region. For example, the data collection unit can analyze the characteristics of events in each region and collect data appropriate to that region. The data collection unit can also identify the success factors of events in each region and collect data accordingly. The data collection unit can also analyze the failure factors of events in each region and find areas for improvement. In this way, by collecting data while considering the characteristics of events in each region, it is possible to collect appropriate data appropriate to each region. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the characteristics of events in each region into the AI, and the AI ​​collects the data.

[0041] The data collection unit can collect relevant data by analyzing social media and online data during data collection. For example, the data collection unit can analyze social media posts to collect event ratings and feedback. The data collection unit can also analyze online reviews to identify success factors and areas for improvement for events. The data collection unit can also integrate social media and online review data to conduct a comprehensive evaluation. This allows for the collection of more diverse data by analyzing social media and online data and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media and online review data into an AI, which then analyzes and collects the data.

[0042] The analysis unit can analyze the success and failure factors of an event in detail during the analysis process and identify areas for improvement. For example, the analysis unit can analyze the success factors of an event in detail and identify the factors for success. The analysis unit can also analyze the failure factors of an event in detail and find areas for improvement. The analysis unit can also compare the success and failure factors to find the optimal method. In this way, by analyzing the success and failure factors of an event in detail, areas for improvement can be identified. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the success and failure factors of an event into the AI, which then analyzes them in detail and identifies areas for improvement.

[0043] The analysis unit can perform analysis while considering the attribute information of event participants. For example, the analysis unit can perform analysis according to the age group of participants and identify methods suitable for that age group. The analysis unit can also perform analysis according to the gender of participants and identify methods suitable for that gender. The analysis unit can also perform analysis according to the interests and preferences of participants and find the optimal method. In this way, by performing analysis while considering the attribute information of event participants, it is possible to identify methods suitable for participants. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the attribute information of participants into the AI, and the AI ​​performs analysis while considering the attribute information.

[0044] The analysis unit can perform analysis while considering the geographical distribution of events. For example, the analysis unit can analyze the characteristics of events in each region and perform analysis appropriate to that region. The analysis unit can also identify the success factors of events in each region and perform analysis. The analysis unit can also analyze the failure factors of events in each region and find areas for improvement. In this way, by performing analysis while considering the geographical distribution of events, appropriate analysis can be performed according to the region. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the characteristics of events in each region into the AI, and the AI ​​performs analysis while considering the geographical distribution.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis by referring to research data. The analysis unit can also integrate literature and research data to perform a comprehensive analysis. This allows for improved accuracy of the analysis by referring to relevant literature and research data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant literature and research data into the AI, which can then improve the accuracy of the analysis.

[0046] The proposal department can adjust the level of detail of a proposal based on the importance of the event. For example, the proposal department will provide a detailed proposal for high-importance events. For low-importance events, the proposal department can also provide a concise proposal. The proposal department can adjust the level of detail of a proposal according to its importance. This allows for the provision of appropriate proposals by adjusting the level of detail based on the importance of the event. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department inputs the importance of the event into the AI, and the AI ​​adjusts the level of detail of the proposal.

[0047] The proposal unit can apply different proposal algorithms depending on the event category when making a proposal. For example, for a dance event, the proposal unit can apply a proposal algorithm specialized for dance. For a theatrical event, the proposal unit can also apply a proposal algorithm specialized for theatrical performances. For a singing event, the proposal unit can also apply a proposal algorithm specialized for singing performances. This allows for more appropriate proposals by applying a proposal algorithm appropriate to the event category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the event category into the AI, and the AI ​​applies a different proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the timing of the events. For example, the proposal department will prioritize proposals for upcoming events. It can also postpone proposals for events that are far in the future. The proposal department can also adjust the priority of proposals according to the timing of the events. This allows proposals to be submitted at the appropriate time by determining the priority of proposals based on the timing of the events. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department inputs the timing of the events into the AI, and the AI ​​determines the priority of proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of events when making a proposal. For example, the proposal unit will prioritize proposals for highly relevant events. The proposal unit can also postpone proposals for less relevant events. The proposal unit can also adjust the order of proposals according to their relevance. This allows for prioritizing highly relevant proposals by adjusting the order of proposals based on the relevance of events. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the relevance of events into the AI, and the AI ​​adjusts the order of proposals.

[0050] The information provision unit can select the optimal information provision method by referring to the user's past usage history when providing information. For example, the information provision unit may prioritize information provision methods that the user has used in the past. The information provision unit can also identify the optimal information provision method from the user's past usage history. The information provision unit can also analyze the user's usage history and select the optimal information provision method. This allows the optimal information provision method to be selected by referring to the user's past usage history. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit may input the user's past usage history into the AI, and the AI ​​may select the optimal information provision method.

[0051] The information provision unit can customize information based on the user's current situation and needs when providing it. For example, if the user is in a hurry, the information provision unit can quickly provide the necessary information. If the user is relaxed, the information provision unit can also provide detailed information. The information provision unit can also customize information according to the user's current situation and needs. This allows the information provision unit to provide the most suitable information for the user by customizing the information based on the user's current situation and needs. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit inputs the user's current situation and needs into the AI, and the AI ​​customizes the information.

[0052] The information provision unit can provide optimal information by considering the user's geographical location information when providing information. For example, the information provision unit can provide optimal information based on the user's current location. The information provision unit can also provide relevant information by considering the user's geographical location information. The information provision unit can also update the user's location information in real time and provide optimal information. In this way, by providing optimal information by considering the user's geographical location information, it is possible to provide information that is useful to the user. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit inputs the user's geographical location information into the AI, and the AI ​​provides optimal information.

[0053] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, the information provision unit can analyze a user's social media posts and provide relevant information. The information provision unit can also analyze a user's social media activity and provide information based on their interests. The information provision unit can also integrate social media data and provide comprehensive information. This allows the information provision unit to provide highly relevant information to the user by analyzing their social media activity. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's social media activity into AI, and the AI ​​can provide relevant information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can select past event data considering factors such as event success and participant satisfaction. For example, it can prioritize collecting data from highly successful events and analyze the factors contributing to that success. It can also collect data from events with high participant satisfaction and identify the factors contributing to that satisfaction. Furthermore, it can collect data from events with low success or low satisfaction to identify areas for improvement. By selecting data based on event success and participant satisfaction, more useful data can be collected.

[0056] The data collection unit can filter data based on the scale of the event and the age range of the participants during data collection. For example, it can prioritize the collection of data from large-scale events to find the optimal method for that scale. It can also collect data based on the age range of the participants to identify methods suitable for that age group. Furthermore, it can collect data from smaller events to find areas for improvement based on the scale. In this way, by filtering data based on the scale of the event and the age range of the participants, appropriate data can be collected.

[0057] The analysis unit can perform a detailed analysis of the success and failure factors of an event during the analysis process, and identify areas for improvement. For example, it can analyze the success factors of an event in detail to identify the factors contributing to its success. It can also analyze the failure factors of an event in detail to find areas for improvement. Furthermore, it can compare the success and failure factors to find the optimal approach. In this way, by analyzing the success and failure factors of an event in detail, areas for improvement can be identified.

[0058] The proposal team can adjust the level of detail in their proposals based on the importance of the event. For example, they can provide detailed proposals for high-priority events and concise proposals for low-priority events. Furthermore, they can adjust the level of detail in the proposal according to the importance of each event. This allows them to provide appropriate proposals by adjusting the level of detail based on the importance of the event.

[0059] The information provision department can select the most suitable information provision method by referring to the user's past usage history when providing information. For example, it can prioritize selecting information provision methods that the user has used in the past. It can also identify the most suitable information provision method from the user's past usage history. Furthermore, it can analyze the user's usage history and select the most suitable information provision method. In this way, the most suitable information provision method can be selected by referring to the user's past usage history.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects past event data. For example, it can collect data such as the type of past event, the duration, and the number of participants. The data collection unit can also use AI to automatically collect past event data. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, based on the collected data, it can determine which performances are popular, which choreography is easy for children to remember, and which casting is effective. The analysis unit can also use AI to analyze the collected data. Step 3: The proposal department proposes the optimal method based on the analysis results obtained by the analysis department. For example, it proposes methods for optimal performances, choreography, casting, practice methods, material procurement, and communication. The proposal department can also use AI to propose the optimal method based on the analysis results. Step 4: The Information Provision Department provides the necessary information based on the methods proposed by the Proposal Department. For example, it centrally manages and provides information such as practice schedules, material suppliers, and contact methods to the school and parents. The Information Provision Department can also use AI to provide the necessary information based on the proposed methods.

[0062] (Example of form 2) The play preparation support system according to an embodiment of the present invention is a system that supports the preparation of play events held regularly at kindergartens and nursery schools. This play preparation support system provides support for efficiently handling many matters that need to be considered, such as selecting a play, choreography, casting, practice methods, procurement of materials, and communication. The play preparation support system collects past event data and analyzes it using AI to propose the optimal method and provide necessary information. This reduces the burden on the school and parents and allows preparations to proceed efficiently. For example, by collecting past event data and analyzing it with AI, the play preparation support system can understand which plays are popular, which choreography is easy for children to remember, and which casting is effective. Based on this, it proposes the optimal play, choreography, and casting. It also proposes the optimal method for practice methods, procurement of materials, and communication based on past data. Furthermore, based on the proposed method, it links various information networks and provides necessary information. For example, it centrally manages practice schedules, material suppliers, and contact methods and provides them to the school and parents. This allows preparations to proceed efficiently and reduces the burden. In this way, the Play Day Preparation Support System is a system designed to support the preparation of play days at kindergartens and daycare centers, reducing the burden on the school and parents, and allowing them to enjoy the process leading up to the play day. This allows the Play Day Preparation Support System to reduce the burden on the school and parents and to proceed with preparations efficiently.

[0063] The school play preparation support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an information provision unit. The data collection unit collects past event data. The data collection unit can collect data such as the type of past event, the duration, and the number of participants. The data collection unit can also automatically collect past event data using AI. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, determine which plays are popular, which choreography is easy for children to remember, and which casting is effective based on the collected data. The analysis unit can also analyze the collected data using AI. The proposal unit proposes the optimal method based on the analysis results obtained by the analysis unit. The proposal unit proposes methods such as the optimal play, choreography, casting, practice methods, material procurement, and communication. The proposal unit can also propose the optimal method based on the analysis results using AI. The information provision unit provides the necessary information based on the method proposed by the proposal unit. The information provision department centrally manages and provides information such as practice schedules, material suppliers, and contact methods to the kindergarten and parents. The information provision department can also use AI to provide necessary information based on suggested methods. As a result, the play preparation support system according to this embodiment reduces the burden on the kindergarten and parents and allows preparations to proceed efficiently.

[0064] The data collection unit collects historical event data. For example, it can collect data such as the type of past event, its duration, and the number of participants. Specifically, it collects details such as the program content of past school plays, the types of performances, the duration of each performance, the ages and number of participating children, parental involvement, and details of costumes and props used. This data is crucial for understanding the success factors and areas for improvement of events. The data collection unit can also use AI to automatically collect historical event data. For example, it can extract and organize necessary information from databases and cloud storage where records of past events are stored. AI can use natural language processing to extract useful information from text data and image recognition to analyze details of performances and choreography from photos and videos. This allows the data collection unit to collect information more efficiently and accurately than manual data collection. Furthermore, the data collection unit can collect relevant data from publicly available information on the internet and social media posts. This allows for the collection of broader information and improved analysis accuracy. In addition, the data collection unit centrally manages the collected data, making it easily accessible to the analysis and proposal units. This will improve the overall efficiency and interoperability of the system.

[0065] The analysis department analyzes the data collected by the data collection department. For example, based on the collected data, the analysis department can determine which performances are popular, which choreography is easy for children to remember, and which casting is effective. Specifically, it statistically analyzes past event data to identify patterns in popular performances and choreography. For example, it can extract the characteristics of performances that are popular with children of a specific age group and choreography that is easy to remember. Regarding casting, it can propose the optimal division of roles based on the children's personalities, special skills, and past performance data. The analysis department can also use AI to analyze the collected data. The AI ​​uses machine learning algorithms to learn patterns and trends from the data and make predictions for future events. For example, based on past data, it can predict which performances are most likely to be successful at the next school play. The AI ​​can also detect anomalies in the data, identify areas where problems occurred in past events, and suggest areas for improvement. In this way, the analysis department can support the preparation of more effective and successful school plays based on the collected data. Furthermore, the analysis department visualizes the data and provides information in a way that is easy for kindergartens and parents to understand. For example, graphs and charts can be used to visually show trends in popular performances and choreography. This allows the analysis department to support data-driven decision-making and promote efficient preparation.

[0066] The proposal department proposes the optimal method based on the analysis results obtained by the analysis department. For example, the proposal department proposes methods for optimal performances and choreography, casting, practice methods, material procurement, and communication. Specifically, based on the analysis results, they select performances and choreography that children can enjoy and easily learn, and propose practice schedules and methods. For example, they propose performances suitable for specific age groups, setting practice times to maintain children's concentration, and effective practice methods. Regarding casting, they propose the optimal division of roles considering the children's special skills and personalities. The proposal department can also use AI to propose the optimal method based on the analysis results. The AI ​​automatically proposes the optimal combination of performances, choreography, and casting based on past data and analysis results. Furthermore, the AI ​​can monitor the progress of practice and propose adjustments to practice methods and schedules as needed. In this way, the proposal department can support efficient and effective preparation and help realize a highly successful school play. In addition, the proposal department also makes proposals regarding material procurement and communication. For example, they propose sources and costs for necessary costumes and props, as well as methods for communication and scheduling. This allows the proposal team to improve the overall efficiency of preparations and reduce the burden on the school and parents.

[0067] The Information Provision Department provides necessary information based on the methods proposed by the Proposal Department. For example, the Information Provision Department centrally manages and provides information such as practice schedules, material suppliers, and contact methods to the school and parents. Specifically, based on the proposed practice schedule, it creates practice plans for each class and group and shares them with parents and other relevant parties. It also creates a list of necessary materials and provides information such as suppliers, costs, and delivery dates. Furthermore, regarding contact methods, it provides means to communicate practice progress and important announcements in a timely manner. The Information Provision Department can also use AI to provide necessary information based on the proposed methods. The AI ​​monitors practice progress in real time and automatically notifies if schedule changes or additional practice are needed. The AI ​​also manages material inventory and supplier information and can automatically order necessary materials before they run out. This allows the Information Provision Department to provide information efficiently and accurately, and to ensure smooth progress in preparations. Furthermore, the Information Provision Department can collect feedback from parents and other relevant parties and continuously improve the proposed content and methods of information provision. For example, it can collect opinions on practice schedules and contact methods and incorporate them into future events. This allows the information department to provide appropriate support based on the latest information at all times, thereby reducing the burden on kindergartens and parents.

[0068] The data collection unit can collect historical event data. For example, it can collect data such as the type of past event, its duration, and the number of participants. The data collection unit can also use AI to automatically collect historical event data. This allows for the provision of data necessary for analysis. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to automatically collect data from publicly available databases on the internet in order to collect historical event data.

[0069] The analysis unit can analyze the collected data to understand which performances are popular, which choreography is easy for children to remember, and which casting is effective. For example, the analysis unit can identify popular performances, easy-to-remember choreography, and effective casting based on the collected data. The analysis unit can also use AI to analyze the collected data. This allows the analysis unit to identify popular performances, easy-to-remember choreography, and effective casting by analyzing the collected data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into the AI, which will analyze the data to identify popular performances, easy-to-remember choreography, and effective casting.

[0070] The proposal department can propose optimal performances, choreography, casting, practice methods, material procurement, and communication methods based on the analysis results. For example, the proposal department can propose optimal performances, choreography, casting, practice methods, material procurement, and communication methods based on the analysis results. The proposal department can also use AI to propose optimal methods based on the analysis results. This allows for efficient preparation by proposing optimal methods based on the analysis results. Some or all of the above-mentioned processes in the proposal department may be performed using AI or not. For example, the proposal department inputs the analysis results into AI, and the AI ​​proposes optimal performances, choreography, casting, practice methods, material procurement, and communication methods.

[0071] The Information Provision Department can centrally manage and provide practice schedules, material suppliers, contact methods, etc., to the kindergarten and parents based on the proposed method. The Information Provision Department can also use AI to provide the necessary information based on the proposed method. This allows for efficient preparation by providing the necessary information based on the proposed method. Some or all of the above processing in the Information Provision Department may be performed using AI or not. For example, the Information Provision Department inputs practice schedules, material suppliers, contact methods, etc., into the AI ​​based on the proposed method, and the AI ​​centrally manages and provides this information to the kindergarten and parents.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay data collection and wait until the user is relaxed. If the user is relaxed, the data collection unit can also collect data quickly and efficiently obtain information. If the user is in a hurry, the data collection unit can also collect data quickly and provide the necessary information immediately. This reduces the burden on the user by adjusting the timing of data collection based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's facial expression data into the generative AI to estimate the user's emotions, and the generative AI estimates the emotions.

[0073] The data collection unit can select data from past events by considering factors such as the success of the event and participant satisfaction. For example, the data collection unit can prioritize collecting data from events with high success rates and analyze the factors contributing to that success. The data collection unit can also collect data from events with high participant satisfaction and identify the factors contributing to that satisfaction. Furthermore, the data collection unit can collect data from events with low success rates and low satisfaction rates to identify areas for improvement. By selecting data based on factors such as event success and participant satisfaction, more useful data can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input event success rates and participant satisfaction into an AI, which then selects the data.

[0074] The data collection unit can filter data based on the scale of the event and the age range of the participants during data collection. For example, the data collection unit can prioritize collecting data from large-scale events and find the optimal method for that scale. The data collection unit can also collect data based on the age range of the participants and identify methods suitable for that age group. The data collection unit can also collect data from small-scale events and find areas for improvement based on the scale. In this way, appropriate data can be collected by filtering data based on the scale of the event and the age range of the participants. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the scale of the event and the age range of the participants into the AI, and the AI ​​filters the data.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit may postpone collecting less important data. If the user is relaxed, the data collection unit may also prioritize collecting more important data. If the user is in a hurry, the data collection unit can quickly collect the necessary data. This allows for efficient data collection by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs user facial expression data into a generative AI to estimate the user's emotions, and the generative AI estimates the emotions.

[0076] The data collection unit can collect data while considering the characteristics of events in each region. For example, the data collection unit can analyze the characteristics of events in each region and collect data appropriate to that region. The data collection unit can also identify the success factors of events in each region and collect data accordingly. The data collection unit can also analyze the failure factors of events in each region and find areas for improvement. In this way, by collecting data while considering the characteristics of events in each region, it is possible to collect appropriate data appropriate to each region. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the characteristics of events in each region into the AI, and the AI ​​collects the data.

[0077] The data collection unit can collect relevant data by analyzing social media and online data during data collection. For example, the data collection unit can analyze social media posts to collect event ratings and feedback. The data collection unit can also analyze online reviews to identify success factors and areas for improvement for events. The data collection unit can also integrate social media and online review data to conduct a comprehensive evaluation. This allows for the collection of more diverse data by analyzing social media and online data and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media and online review data into an AI, which then analyzes and collects the data.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, in order to estimate the user's emotions, the analysis unit inputs the user's facial expression data into the generative AI, and the generative AI estimates the emotions.

[0079] The analysis unit can analyze the success and failure factors of an event in detail during the analysis process and identify areas for improvement. For example, the analysis unit can analyze the success factors of an event in detail and identify the factors for success. The analysis unit can also analyze the failure factors of an event in detail and find areas for improvement. The analysis unit can also compare the success and failure factors to find the optimal method. In this way, by analyzing the success and failure factors of an event in detail, areas for improvement can be identified. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the success and failure factors of an event into the AI, which then analyzes them in detail and identifies areas for improvement.

[0080] The analysis unit can perform analysis while considering the attribute information of event participants. For example, the analysis unit can perform analysis according to the age group of participants and identify methods suitable for that age group. The analysis unit can also perform analysis according to the gender of participants and identify methods suitable for that gender. The analysis unit can also perform analysis according to the interests and preferences of participants and find the optimal method. In this way, by performing analysis while considering the attribute information of event participants, it is possible to identify methods suitable for participants. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the attribute information of participants into the AI, and the AI ​​performs analysis while considering the attribute information.

[0081] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit may postpone less important analysis results. If the user is relaxed, the analysis unit may also prioritize displaying more important analysis results. If the user is in a hurry, the analysis unit may also quickly display the necessary analysis results. This allows for the prioritization of important analysis results by determining the priority of analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the user's facial expression data into the generative AI to estimate the user's emotions, and the generative AI estimates the emotions.

[0082] The analysis unit can perform analysis while considering the geographical distribution of events. For example, the analysis unit can analyze the characteristics of events in each region and perform analysis appropriate to that region. The analysis unit can also identify the success factors of events in each region and perform analysis. The analysis unit can also analyze the failure factors of events in each region and find areas for improvement. In this way, by performing analysis while considering the geographical distribution of events, appropriate analysis can be performed according to the region. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the characteristics of events in each region into the AI, and the AI ​​performs analysis while considering the geographical distribution.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis by referring to research data. The analysis unit can also integrate literature and research data to perform a comprehensive analysis. This allows for improved accuracy of the analysis by referring to relevant literature and research data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant literature and research data into the AI, which can then improve the accuracy of the analysis.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present simple and easily visible suggestions. If the user is relaxed, the suggestion unit can also present suggestions that include detailed information. If the user is in a hurry, the suggestion unit can present suggestions that are to the point. By adjusting the way suggestions are presented based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit may input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI may estimate the emotions.

[0085] The proposal department can adjust the level of detail of a proposal based on the importance of the event. For example, the proposal department will provide a detailed proposal for high-importance events. For low-importance events, the proposal department can also provide a concise proposal. The proposal department can adjust the level of detail of a proposal according to its importance. This allows for the provision of appropriate proposals by adjusting the level of detail based on the importance of the event. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department inputs the importance of the event into the AI, and the AI ​​adjusts the level of detail of the proposal.

[0086] The proposal unit can apply different proposal algorithms depending on the event category when making a proposal. For example, for a dance event, the proposal unit can apply a proposal algorithm specialized for dance. For a theatrical event, the proposal unit can also apply a proposal algorithm specialized for theatrical performances. For a singing event, the proposal unit can also apply a proposal algorithm specialized for singing performances. This allows for more appropriate proposals by applying a proposal algorithm appropriate to the event category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the event category into the AI, and the AI ​​applies a different proposal algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a visually stimulating suggestion. By adjusting the length of the suggestion based on the user's emotions, the suggestion unit can provide a suggestion of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit may input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI may estimate the emotions.

[0088] The proposal department can determine the priority of proposals based on the timing of the events. For example, the proposal department will prioritize proposals for upcoming events. It can also postpone proposals for events that are far in the future. The proposal department can also adjust the priority of proposals according to the timing of the events. This allows proposals to be submitted at the appropriate time by determining the priority of proposals based on the timing of the events. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department inputs the timing of the events into the AI, and the AI ​​determines the priority of proposals.

[0089] The proposal unit can adjust the order of proposals based on the relevance of events when making a proposal. For example, the proposal unit will prioritize proposals for highly relevant events. The proposal unit can also postpone proposals for less relevant events. The proposal unit can also adjust the order of proposals according to their relevance. This allows for prioritizing highly relevant proposals by adjusting the order of proposals based on the relevance of events. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the relevance of events into the AI, and the AI ​​adjusts the order of proposals.

[0090] The information delivery unit can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is nervous, the information delivery unit can provide simple and highly visible information. If the user is relaxed, the information delivery unit can also provide information that includes details. If the user is in a hurry, the information delivery unit can also provide information that gets straight to the point. In this way, by adjusting the method of information delivery based on the user's emotions, information can be delivered in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not using AI. For example, in order to estimate the user's emotions, the information delivery unit inputs the user's facial expression data into the generative AI, and the generative AI estimates the emotions.

[0091] The information provision unit can select the optimal information provision method by referring to the user's past usage history when providing information. For example, the information provision unit may prioritize information provision methods that the user has used in the past. The information provision unit can also identify the optimal information provision method from the user's past usage history. The information provision unit can also analyze the user's usage history and select the optimal information provision method. This allows the optimal information provision method to be selected by referring to the user's past usage history. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit may input the user's past usage history into the AI, and the AI ​​may select the optimal information provision method.

[0092] The information provision unit can customize information based on the user's current situation and needs when providing it. For example, if the user is in a hurry, the information provision unit can quickly provide the necessary information. If the user is relaxed, the information provision unit can also provide detailed information. The information provision unit can also customize information according to the user's current situation and needs. This allows the information provision unit to provide the most suitable information for the user by customizing the information based on the user's current situation and needs. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit inputs the user's current situation and needs into the AI, and the AI ​​customizes the information.

[0093] The information delivery unit can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the information delivery unit may postpone providing less important information. If the user is relaxed, the information delivery unit may also prioritize providing more important information. If the user is in a hurry, the information delivery unit may also quickly provide the necessary information. In this way, by prioritizing information delivery based on the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not using AI. For example, in order to estimate the user's emotions, the information delivery unit inputs the user's facial expression data into the generative AI, and the generative AI estimates the emotions.

[0094] The information provision unit can provide optimal information by considering the user's geographical location information when providing information. For example, the information provision unit can provide optimal information based on the user's current location. The information provision unit can also provide relevant information by considering the user's geographical location information. The information provision unit can also update the user's location information in real time and provide optimal information. In this way, by providing optimal information by considering the user's geographical location information, it is possible to provide information that is useful to the user. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit inputs the user's geographical location information into the AI, and the AI ​​provides optimal information.

[0095] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, the information provision unit can analyze a user's social media posts and provide relevant information. The information provision unit can also analyze a user's social media activity and provide information based on their interests. The information provision unit can also integrate social media data and provide comprehensive information. This allows the information provision unit to provide highly relevant information to the user by analyzing their social media activity. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's social media activity into AI, and the AI ​​can provide relevant information.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, data collection can be delayed until the user is relaxed. If the user is relaxed, data collection can be performed quickly to efficiently obtain information. Furthermore, if the user is in a hurry, data collection can be performed quickly to provide the necessary information immediately. In this way, adjusting the timing of data collection based on the user's emotions can reduce the burden on the user.

[0098] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the analysis results based on the user's emotions, a display method that is easy for the user to understand can be provided.

[0099] The proposal function can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is nervous, it can present a simple and highly visible proposal. If the user is relaxed, it can present a proposal that includes detailed information. Furthermore, if the user is in a hurry, it can present a proposal that gets straight to the point. In this way, by adjusting the way the proposal is presented based on the user's emotions, it is possible to present proposals that are easy for the user to understand.

[0100] The information delivery unit can estimate the user's emotions and adjust the method of information delivery based on those emotions. For example, if the user is stressed, it can provide simple and highly visible information. If the user is relaxed, it can provide information that includes more details. Furthermore, if the user is in a hurry, it can provide information that gets straight to the point. In this way, by adjusting the method of information delivery based on the user's emotions, it is possible to provide information that is easy for the user to understand.

[0101] The data collection unit can select past event data considering factors such as event success and participant satisfaction. For example, it can prioritize collecting data from highly successful events and analyze the factors contributing to that success. It can also collect data from events with high participant satisfaction and identify the factors contributing to that satisfaction. Furthermore, it can collect data from events with low success or low satisfaction to identify areas for improvement. By selecting data based on event success and participant satisfaction, more useful data can be collected.

[0102] The data collection unit can filter data based on the scale of the event and the age range of the participants during data collection. For example, it can prioritize the collection of data from large-scale events to find the optimal method for that scale. It can also collect data based on the age range of the participants to identify methods suitable for that age group. Furthermore, it can collect data from smaller events to find areas for improvement based on the scale. In this way, by filtering data based on the scale of the event and the age range of the participants, appropriate data can be collected.

[0103] The analysis unit can perform a detailed analysis of the success and failure factors of an event during the analysis process, and identify areas for improvement. For example, it can analyze the success factors of an event in detail to identify the factors contributing to its success. It can also analyze the failure factors of an event in detail to find areas for improvement. Furthermore, it can compare the success and failure factors to find the optimal approach. In this way, by analyzing the success and failure factors of an event in detail, areas for improvement can be identified.

[0104] The proposal team can adjust the level of detail in their proposals based on the importance of the event. For example, they can provide detailed proposals for high-priority events and concise proposals for low-priority events. Furthermore, they can adjust the level of detail in the proposal according to the importance of each event. This allows them to provide appropriate proposals by adjusting the level of detail based on the importance of the event.

[0105] The information provision department can select the most suitable information provision method by referring to the user's past usage history when providing information. For example, it can prioritize selecting information provision methods that the user has used in the past. It can also identify the most suitable information provision method from the user's past usage history. Furthermore, it can analyze the user's usage history and select the most suitable information provision method. In this way, the most suitable information provision method can be selected by referring to the user's past usage history.

[0106] The information delivery unit can estimate the user's emotions and determine the priority of information delivery based on those emotions. For example, if a user is stressed, less important information can be postponed. If a user is relaxed, highly important information can be prioritized. Furthermore, if a user is in a hurry, necessary information can be delivered quickly. In this way, by determining the priority of information delivery based on the user's emotions, important information can be delivered preferentially.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects past event data. For example, it can collect data such as the type of past event, the duration, and the number of participants. The data collection unit can also use AI to automatically collect past event data. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, based on the collected data, it can determine which performances are popular, which choreography is easy for children to remember, and which casting is effective. The analysis unit can also use AI to analyze the collected data. Step 3: The proposal department proposes the optimal method based on the analysis results obtained by the analysis department. For example, it proposes methods for optimal performances, choreography, casting, practice methods, material procurement, and communication. The proposal department can also use AI to propose the optimal method based on the analysis results. Step 4: The Information Provision Department provides the necessary information based on the methods proposed by the Proposal Department. For example, it centrally manages and provides information such as practice schedules, material suppliers, and contact methods to the school and parents. The Information Provision Department can also use AI to provide the necessary information based on the proposed methods.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data acquisition unit, analysis unit, proposal unit, and information provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data acquisition unit collects past event data using the camera 42 and communication I / F 44 of the smart device 14 and manages the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal method based on the analysis results. The information provision unit is implemented in the control unit 46A of the smart device 14 and provides the necessary information based on the proposed method. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data acquisition unit, analysis unit, proposal unit, and information provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data acquisition unit collects past event data using the camera 42 and communication I / F 44 of the smart glasses 214 and manages the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal method based on the analysis results. The information provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the necessary information based on the proposed method. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data acquisition unit, analysis unit, proposal unit, and information provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data acquisition unit collects past event data using the camera 42 and communication I / F 44 of the headset terminal 314 and manages the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal method based on the analysis results. The information provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the necessary information based on the proposed method. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data acquisition unit, analysis unit, proposal unit, and information provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data acquisition unit collects past event data using the camera 42 and communication I / F 44 of the robot 414 and manages the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal method based on the analysis results. The information provision unit is implemented in the control unit 46A of the robot 414 and provides the necessary information based on the proposed method. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The data collection unit collects past event data, An analysis unit analyzes the data collected by the data acquisition unit, A proposal unit proposes the optimal method based on the analysis results obtained by the analysis unit, The system includes an information providing unit that provides necessary information based on the method proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned data acquisition unit is Collect past event data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing the collected data, we can understand which performances are popular, which choreography is easy for children to remember, and which casting choices are effective. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we will propose the optimal program, choreography, casting, rehearsal methods, material procurement, and communication strategies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned information provision unit, Based on the proposed method, practice schedules, material suppliers, and contact methods will be centrally managed and provided to the school and parents. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data acquisition unit is When collecting past event data, the data is selected based on factors such as the success of the event and participant satisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data acquisition unit is When collecting data, filter the data based on the scale of the event and the age range of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data acquisition unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data acquisition unit is When collecting data, consider the characteristics of events in each region. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data acquisition unit is When collecting data, analyze social media and online data and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, we will thoroughly analyze the success and failure factors of the event and identify areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the attribute information of the event participants will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the geographical distribution of events will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we refer to relevant literature and research data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are 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 event. 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 event category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the 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 the events. 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 the events. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned information provision unit, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned information provision unit, When providing information, the system selects the most suitable method of information delivery by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned information provision unit, When providing information, customize the information based on the user's current situation and needs. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned information provision unit, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned information provision unit, When providing information, we will consider the user's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned information provision unit, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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. The data collection unit collects past event data, An analysis unit analyzes the data collected by the data acquisition unit, Based on the analysis results obtained by the analysis unit, the proposal unit proposes the optimal method, The system includes an information providing unit that provides necessary information based on the method proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit, The collected data is analyzed to understand which performances are popular, which choreography is easy for children to remember, and which casting is effective. The system according to feature 1.

3. The aforementioned proposal section is, Based on the analysis results, we will propose the optimal program, choreography, casting, rehearsal methods, material procurement, and communication strategies. The system according to feature 1.

4. The aforementioned information provision unit, Based on the proposed method, practice schedules, material suppliers, and contact methods will be centrally managed and provided to the school and parents. The system according to feature 1.

5. The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

6. The aforementioned data acquisition unit is When collecting past event data, the data is selected based on factors such as the success of the event and participant satisfaction. The system according to feature 1.

7. The aforementioned data acquisition unit is When collecting data, filter the data based on the scale of the event and the age range of the participants. The system according to feature 1.

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