system

Abstract

The system according to this embodiment aims to streamline the entire program production process and reduce the burden of communication and coordination among staff. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a change response unit, and a sponsor coordination unit. The data collection unit collects data necessary for program production. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an appropriate schedule, cast, and filming locations based on the analysis results obtained by the analysis unit. The change response unit responds when changes occur based on the content proposed by the proposal unit. The sponsor coordination unit coordinates with sponsors.

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 as a 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, processes such as schedule adjustment, casting, and search and booking of filming locations in program production are complicated, and there is a problem that the communication and coordination load among staff is high.

[0005] The system according to the embodiment aims to streamline the entire process of program production and reduce the communication and coordination load among staff.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a change response unit, and a sponsor coordination unit. The data collection unit collects data necessary for program production. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an appropriate schedule, cast, and filming locations based on the analysis results obtained by the analysis unit. The change response unit responds when changes occur based on the content proposed by the proposal unit. The sponsor coordination unit coordinates with sponsors. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the entire program production process and reduce the burden of communication and coordination among staff. [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 such as 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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 integrated management AI agent system according to an embodiment of the present invention is a system that streamlines the entire process of program production. This integrated management AI agent system reduces the burden of communication and coordination among staff by integrating schedule adjustment, casting support, location search and booking into a single AI agent. The integrated management AI agent system collects various data necessary for program production. For example, it collects data such as cast schedules, location availability, and staff schedules. Next, the AI ​​analyzes the collected data and proposes the optimal schedule, cast, and location. For example, it proposes the optimal filming schedule considering the cast's schedule and location availability. Furthermore, the integrated management AI agent system also responds when changes occur. For example, if a cast member's schedule changes, the AI ​​automatically proposes a new schedule and notifies the relevant parties. This reduces the burden on each person in charge and allows the production process to proceed smoothly. The integrated management AI agent system also coordinates with sponsors. For example, it adjusts the program content and schedule based on the sponsor's requests. This can improve sponsor satisfaction. In this way, by using the integrated management AI agent system, the entire process of program production can be streamlined and the burden of communication and coordination among staff can be reduced. This will improve the quality of program production. The integrated AI agent system will streamline the entire program production process and reduce the burden of communication and coordination among staff.

[0029] The integrated management AI agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a change response unit, and a sponsor coordination unit. The data collection unit collects data necessary for program production. Data necessary for program production includes, but is not limited to, cast schedules, location availability, and staff schedules. For example, the data collection unit obtains cast schedules from an online schedule management system. The data collection unit can also obtain location availability from an online reservation system. Furthermore, the data collection unit can obtain staff schedules from a project management tool. For example, the data collection unit obtains cast schedules in real time from an online schedule management system. Location availability is automatically obtained from an online reservation system. Staff schedules are updated periodically from a project management tool. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit proposes the optimal schedule, cast, and location based on the collected data. For example, the analysis unit proposes the optimal filming schedule considering the cast schedule and location availability. Furthermore, the analysis unit can evaluate the popularity of the cast based on the collected data and propose the most suitable cast. In addition, the analysis unit can propose the most suitable filming locations by considering the availability of those locations. For example, the analysis unit proposes the optimal filming schedule based on the cast's schedules and the availability of the filming locations. It evaluates the popularity of the cast and proposes the most suitable cast. It proposes the most suitable filming locations by considering the availability of those locations. The proposal unit proposes the optimal schedule, cast, and filming locations based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the optimal filming schedule by considering the cast's schedules and the availability of the filming locations. For example, the proposal unit proposes the optimal filming schedule by considering the cast's schedules and the availability of the filming locations. Furthermore, the proposal unit can propose the optimal filming schedule by considering the cast's schedules and the availability of the filming locations. In addition, the proposal unit can propose the optimal filming schedule based on the cast's schedules and the availability of the filming locations.For example, the proposal department proposes the optimal filming schedule based on the cast's schedules and the availability of filming locations. The change response department responds to any changes that occur based on the proposals made by the proposal department. For example, if a cast member's schedule changes, the change response department proposes a new schedule and notifies the relevant parties. The change response department also proposes a new schedule and notifies the relevant parties if a cast member's schedule changes. Furthermore, the change response department can also propose a new schedule and notify the relevant parties if a cast member's schedule changes. For example, if a cast member's schedule changes, the change response department proposes a new schedule and notifies the relevant parties if a cast member's schedule changes. The sponsor coordination department coordinates with sponsors. The sponsor coordination department can, for example, adjust program content and schedules based on sponsor requests. The sponsor coordination department can, for example, adjust program content and schedules based on sponsor requests. Furthermore, the sponsor coordination department can also adjust program content and schedules based on sponsor requests. For example, the sponsor coordination department can adjust program content and schedules based on sponsor requests. The program content and schedules can be adjusted based on sponsor requests. The program content and schedules can be adjusted based on sponsor requests. As a result, the integrated management AI agent system according to the embodiment can streamline the entire program production process and reduce the communication and coordination burden among staff.

[0030] The data collection department collects data necessary for program production. This data includes, but is not limited to, cast schedules, location availability, and staff schedules. For example, the department might obtain cast schedules from an online scheduling management system. It could also obtain location availability from an online booking system. Furthermore, it could obtain staff schedules from a project management tool. For instance, the department might obtain cast schedules in real time from an online scheduling management system. Location availability is automatically obtained from an online booking system. Staff schedules are regularly updated from a project management tool. The data collection department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data could be stored on a cloud server and made accessible to the analysis and proposal departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection department to collect data efficiently and effectively, improving the overall system performance. Furthermore, the data collection department can leverage AI to automate the data collection process and reduce human error. For example, the AI ​​automatically detects changes in cast schedules and collects the latest information. It also automatically retrieves updated information and reflects it in the system if the availability of filming locations changes. This ensures that the data collection unit always maintains the most up-to-date data, allowing for smooth progress in program production planning.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit proposes the optimal schedule, cast, and filming locations based on the collected data. For example, the analysis unit proposes the optimal filming schedule considering the cast's schedule and the availability of filming locations. The analysis unit can also evaluate the popularity of the cast based on the collected data and propose the most suitable cast. Furthermore, the analysis unit can propose the optimal filming locations considering the available time of the locations. For example, the analysis unit proposes the optimal filming schedule based on the cast's schedule and the availability of filming locations. It evaluates the popularity of the cast and proposes the most suitable cast. It proposes the optimal filming locations considering the available time of the locations. The analysis unit uses AI to analyze this data and simulate multiple scenarios to find the most efficient combination of schedule and cast. For example, the AI ​​receives the cast's schedule and the availability of filming locations as input data and calculates the optimal filming schedule. The AI ​​also selects cast members that will attract viewers' attention based on their popularity and past appearances. Furthermore, the AI ​​proposes the optimal filming locations considering the available time of the locations and their accessibility. This allows the analysis unit to quickly and accurately analyze collected data, maximizing the efficiency of program production. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, it can analyze past filming schedules and fluctuations in cast popularity to help plan future program productions. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early problem detection. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The proposal department proposes the optimal schedule, cast, and filming locations based on the analysis results obtained by the analysis department. For example, the proposal department proposes the optimal filming schedule by considering the cast's schedules and the availability of filming locations. The proposal department proposes the optimal filming schedule based on the cast's schedules and the availability of filming locations. The proposal department can also propose the optimal filming schedule by considering the cast's schedules and the availability of filming locations. Furthermore, the proposal department can also propose the optimal filming schedule based on the cast's schedules and the availability of filming locations. For example, the proposal department proposes the optimal filming schedule by considering the cast's schedules and the availability of filming locations. The proposal department proposes the optimal filming schedule by considering the cast's schedules and the availability of filming locations. The proposal department uses AI to make optimal proposals based on the data provided by the analysis department. For example, the AI ​​receives the cast's schedules and the availability of filming locations as input data and calculates the optimal filming schedule. The AI ​​also selects cast members that will attract viewers' attention based on their popularity and past appearances. Furthermore, the AI ​​suggests optimal filming locations, taking into account factors such as availability and accessibility. This allows the suggestion department to quickly and accurately analyze collected data, maximizing the efficiency of program production. In addition, the suggestion department can utilize historical data and statistics to conduct long-term trend analysis and risk assessment. For example, it can analyze past filming schedules and fluctuations in cast popularity to help plan future program productions. The suggestion department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early problem identification. This allows the suggestion department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0033] The Change Response Department will respond to changes based on the proposals made by the Proposal Department. For example, if a cast member's schedule changes, the Change Response Department will propose a new schedule and notify the relevant parties. The Change Response Department can also propose a new schedule and notify the relevant parties if a cast member's schedule changes. Furthermore, the Change Response Department can also propose a new schedule and notify the relevant parties if a cast member's schedule changes. For example, if a cast member's schedule changes, the Change Response Department will propose a new schedule and notify the relevant parties. If a cast member's schedule changes, the Change Response Department will propose a new schedule and notify the relevant parties. If a cast member's schedule changes, the Change Response Department will propose a new schedule and notify the relevant parties. If a cast member's schedule changes, the Change Response Department will propose a new schedule and notify the relevant parties. The Change Response Department will use AI to respond quickly when changes occur. For example, the AI ​​will automatically detect changes in a cast member's schedule and calculate a new schedule. The AI ​​will also automatically send notifications to relevant parties to inform them of the schedule change. Furthermore, the AI ​​will evaluate the impact of the change and make necessary adjustments. For example, if a cast schedule change affects location bookings, the AI ​​checks the availability of new locations and suggests the most suitable ones. This allows the change management department to respond to changes quickly and accurately, supporting the smooth progress of program production. Furthermore, the change management department can analyze past change history and predict future change risks. For example, it can analyze patterns of past schedule changes and assess the change risks for specific cast members or locations. This allows the change management department to identify risks in advance and take preventative measures.

[0034] The Sponsor Coordination Department handles coordination with sponsors. For example, the Sponsor Coordination Department adjusts program content and schedules based on sponsor requests. The Sponsor Coordination Department can also adjust program content and schedules based on sponsor requests. Furthermore, the Sponsor Coordination Department can also adjust program content and schedules based on sponsor requests. For example, the Sponsor Coordination Department adjusts program content and schedules based on sponsor requests. The Sponsor Coordination Department uses AI to efficiently manage sponsor requests and reflect them in program production. For example, AI automatically analyzes sponsor requests and applies them to program content and schedules. AI also simulates multiple scenarios based on sponsor requests and proposes the optimal adjustment plan. Furthermore, AI automates communication with sponsors and responds quickly to changes or additions in requests. This allows the Sponsor Coordination Department to accurately reflect sponsor requests and improve the efficiency of program production. Furthermore, the sponsor coordination department can analyze past sponsor requests and predict future requests. For example, by analyzing patterns in past sponsor requests, they can understand the program content and schedules preferred by specific sponsors. This allows the sponsor coordination department to predict sponsor requests in advance and reflect them in program production plans.

[0035] The data collection unit can collect data on cast schedules, location availability, and staff schedules. For example, the data collection unit can obtain cast schedules from an online schedule management system. The data collection unit can also obtain location availability from an online booking system. The data collection unit can also obtain staff schedules from a project management tool. This allows for the efficient collection of data necessary for program production. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can obtain cast schedules in real time from an online schedule management system and analyze the data using AI.

[0036] The analysis unit can analyze the collected data and propose appropriate schedules, casts, and filming locations. For example, the analysis unit can propose the optimal schedule, cast, and filming locations based on the collected data. For example, the analysis unit can propose the optimal filming schedule by considering the cast's schedules and the availability of filming locations. The analysis unit can also evaluate the popularity of the cast based on the collected data and propose the most suitable cast. Furthermore, the analysis unit can propose the most suitable filming locations by considering the available time for filming locations. In this way, by analyzing the collected data, the optimal schedule, cast, and filming locations can be proposed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can propose the optimal schedule, cast, and filming locations.

[0037] The proposal unit can propose an appropriate filming schedule based on the cast's schedules and the availability of filming locations. The proposal unit can, for example, propose the optimal filming schedule by considering the cast's schedules and the availability of filming locations. The proposal unit can, for example, propose the optimal filming schedule based on the cast's schedules and the availability of filming locations. The proposal unit can also propose the optimal filming schedule by considering the cast's schedules and the availability of filming locations. This allows the proposal unit to propose the optimal filming schedule by considering the cast's schedules and the availability of filming locations. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the cast's schedules and the availability of filming locations into AI, and the AI ​​can propose the optimal filming schedule.

[0038] The change response unit can propose a new schedule and notify relevant parties when a cast member's schedule changes. For example, the change response unit can propose a new schedule and notify relevant parties when a cast member's schedule changes. The change response unit can also propose a new schedule and notify relevant parties when a cast member's schedule changes. This allows for a quick response when a cast member's schedule changes. Some or all of the above processing in the change response unit may be performed using AI, for example, or not using AI. For example, the change response unit can input information about changes in the cast member's schedule into AI, which can then propose a new schedule and notify relevant parties.

[0039] The sponsor coordination unit can adjust program content and schedules based on sponsor requests. The sponsor coordination unit can, for example, adjust program content and schedules based on sponsor requests. The sponsor coordination unit can, for example, adjust program content and schedules based on sponsor requests. The sponsor coordination unit can also adjust program content and schedules based on sponsor requests. This allows for adjustment of program content and schedules based on sponsor requests. Some or all of the above-described processes in the sponsor coordination unit may be performed using AI, for example, or without AI. For example, the sponsor coordination unit can input sponsor requests into AI, and the AI ​​can adjust program content and schedules.

[0040] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and apply that method. For example, the data collection unit can analyze past data collection history and select the optimal timing to avoid data duplication. For example, the data collection unit can adjust the types of data collected during specific time periods based on past data collection history. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, and the AI ​​can select the optimal collection method.

[0041] The data collection unit can filter data based on the current project and areas of interest during data collection. For example, the data collection unit can collect only data related to the current project and eliminate unnecessary data. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's areas of interest. For example, the data collection unit can dynamically change the types of data required according to the progress of the project. This allows for the priority collection of highly relevant data based on the current project and areas of interest. 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 information about the current project and areas of interest into the AI, which can then filter the data collection.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of nearby filming location information based on the current location. For example, the data collection unit can collect cast schedules related to a specific region based on geographical location information. For example, the data collection unit can collect data on optimal travel routes by considering geographical location information. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze trends on social media and collect the schedules of relevant cast members. For example, the data collection unit can collect information on popular filming locations based on social media activity. For example, the data collection unit can analyze the interests of users on social media and prioritize the collection of relevant data. This allows for the priority collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into AI, and the AI ​​can collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows the level of detail of the analysis to be adjusted based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a schedule optimization algorithm to cast schedule data. For example, the analysis unit can apply a location availability prediction algorithm to location availability data. For example, the analysis unit can apply a schedule adjustment algorithm to staff schedule data. This allows the appropriate analysis algorithm to be applied depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of data submitted earlier. For example, the analysis unit may postpone the analysis of data submitted later. For example, the analysis unit may adjust the analysis schedule based on the submission date. This allows the analysis priority to be determined based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into the AI, and the AI ​​can determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically change the order of analysis based on the relevance of the data. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0048] The proposal unit can adjust the level of detail of its proposals based on the importance of the data. For example, the proposal unit can provide detailed proposals for highly important data, or simplified proposals for less important data. The proposal unit can also prioritize proposals based on the importance of the data, thereby adjusting the level of detail of the proposals based on the importance of the data. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance of the data into the AI, which can then adjust the level of detail of the proposals.

[0049] The proposal unit can apply different proposal algorithms depending on the data category when making a proposal. For example, the proposal unit can apply a schedule optimization algorithm to cast schedule data. For example, the proposal unit can apply an availability prediction algorithm to location availability data. For example, the proposal unit can apply a schedule adjustment algorithm to staff schedule data. This allows the appropriate proposal algorithm to be applied depending on the data category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the data category into the AI, and the AI ​​can apply an appropriate proposal algorithm.

[0050] The proposal department can determine the priority of proposals based on the data submission timing. For example, the proposal department may prioritize proposals for data submitted earlier. For example, the proposal department may postpone proposals for data submitted later. The proposal department may adjust the proposal schedule based on the submission timing. This allows the proposal priority to be determined based on the data submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the data submission timing into the AI, and the AI ​​can determine the proposal priority.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the data. For example, the proposal unit can prioritize proposals for highly relevant data. For example, the proposal unit can postpone proposals for less relevant data. For example, the proposal unit can dynamically change the order of proposals based on the relevance of the data. This allows the order of proposals to be adjusted based on the relevance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of proposals.

[0052] The change response unit can analyze past change history to select the optimal response method when responding to a change. For example, the change response unit can identify the most effective response method from past change history and apply that method. For example, the change response unit can analyze past change history and select the optimal response procedure for similar changes. For example, the change response unit can customize response methods to suit specific situations based on past change history. This allows the optimal response method to be selected by analyzing past change history. Some or all of the above processes in the change response unit may be performed using AI, for example, or without AI. For example, the change response unit can input past change history into AI, and the AI ​​can select the optimal response method.

[0053] The change response unit can customize the response measures based on the current project status when a change is addressed. For example, the change response unit can select the optimal response measures according to the current project progress. For example, the change response unit can adjust the response measures based on project priorities. For example, the change response unit can customize the response measures considering the current project resource situation. This allows the response measures to be customized based on the current project status. Some or all of the above processes in the change response unit may be performed using AI, for example, or without AI. For example, the change response unit can input the current project status into the AI, and the AI ​​can customize the response measures.

[0054] The change response unit can select the optimal response method when responding to a change, taking geographical location information into consideration. For example, the change response unit can select a response method that utilizes nearby resources based on the current location. For example, the change response unit can select a response method related to a specific region based on geographical location information. For example, the change response unit can select a response method regarding the optimal travel route, taking geographical location information into consideration. This allows for the selection of the optimal response method by taking geographical location information into consideration. Some or all of the above-described processes in the change response unit may be performed using AI, for example, or without using AI. For example, the change response unit can input geographical location information into AI, and the AI ​​can select the optimal response method.

[0055] The change response unit can analyze social media activity and propose response measures when responding to changes. For example, the change response unit can analyze trends on social media and propose relevant response measures. For example, the change response unit can propose popular response measures based on social media activity. For example, the change response unit can analyze user interests on social media and preferentially propose relevant response measures. In this way, relevant response measures can be proposed by analyzing social media activity. Some or all of the above processing in the change response unit may be performed using AI, for example, or without AI. For example, the change response unit can input social media activity data into AI, and the AI ​​can propose relevant response measures.

[0056] The sponsor adjustment unit can analyze past sponsor history to select the optimal adjustment method during sponsor adjustment. For example, the sponsor adjustment unit can identify the most effective adjustment method from past sponsor history and apply that method. For example, the sponsor adjustment unit can analyze past sponsor history and select the optimal adjustment procedure for similar sponsors. For example, the sponsor adjustment unit can customize the adjustment method to suit a specific situation based on past sponsor history. This allows the optimal adjustment method to be selected by analyzing past sponsor history. Some or all of the above processes in the sponsor adjustment unit may be performed using AI, for example, or without AI. For example, the sponsor adjustment unit can input past sponsor history into AI, and the AI ​​can select the optimal adjustment method.

[0057] The sponsor coordination unit can customize the coordination means based on the current project status when coordinating sponsors. For example, the sponsor coordination unit can select the optimal coordination means according to the current project progress. For example, the sponsor coordination unit can adjust the coordination means based on project priority. For example, the sponsor coordination unit can customize the coordination means considering the current project resource status. This allows the coordination means to be customized based on the current project status. Some or all of the above processes in the sponsor coordination unit may be performed using AI, for example, or without AI. For example, the sponsor coordination unit can input the current project status into AI, and the AI ​​can customize the coordination means.

[0058] The sponsor coordination unit can select the optimal coordination method when coordinating sponsors, taking geographical location information into consideration. For example, the sponsor coordination unit can select a coordination method that utilizes nearby resources based on the current location. For example, the sponsor coordination unit can select coordination means related to a specific region based on geographical location information. For example, the sponsor coordination unit can select a coordination method for the optimal travel route, taking geographical location information into consideration. This allows for the selection of the optimal coordination method, taking geographical location information into consideration. Some or all of the above processing in the sponsor coordination unit may be performed using AI, for example, or without AI. For example, the sponsor coordination unit can input geographical location information into AI, and the AI ​​can select the optimal coordination method.

[0059] The sponsor coordination unit can analyze social media activity and propose coordination measures when coordinating sponsorships. For example, the sponsor coordination unit can analyze trends on social media and propose relevant coordination measures. For example, the sponsor coordination unit can propose popular coordination measures based on social media activity. For example, the sponsor coordination unit can analyze user interests on social media and preferentially propose relevant coordination measures. In this way, relevant coordination measures can be proposed by analyzing social media activity. Some or all of the above processes in the sponsor coordination unit may be performed using AI, for example, or without AI. For example, the sponsor coordination unit can input social media activity data into AI, and the AI ​​can propose relevant coordination measures.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The integrated management AI agent system can also include a budget management department. This department can collect budget data necessary for program production and, in cooperation with the analysis department, propose the optimal budget allocation. For example, the budget management department collects data such as cast salaries, location usage fees, and staff personnel costs, and the analysis department proposes the optimal budget allocation based on this data. Furthermore, if budget changes occur, the budget management department can work with the change response department to propose a new budget allocation and notify relevant parties. This allows the budget management department to streamline program production budget management and reduce waste.

[0062] The integrated management AI agent system can also include a risk management department. This department can collect risk factors in program production and, in cooperation with the analysis department, conduct risk assessments. For example, the risk management department collects data such as weather information, the health status of the cast, and the safety of filming locations, and the analysis department uses this data to conduct risk assessments. Furthermore, if a risk occurs, the risk management department can work with the change response department to propose risk mitigation measures and notify relevant parties. This allows the risk management department to streamline risk management in program production and minimize the impact of risks.

[0063] The integrated management AI agent system can also include a quality control department. This department can collect quality data during program production and, in cooperation with the analysis department, perform quality evaluations. For example, the quality control department can collect data such as image quality of filmed footage, audio clarity, and editing accuracy, and the analysis department can use this data to perform quality evaluations. Furthermore, if quality issues arise, the quality control department can collaborate with the change response department to propose quality improvement measures and notify relevant parties. This allows the quality control department to streamline quality control in program production and improve program quality.

[0064] The integrated management AI agent system can also include a marketing department. This department can collect viewer data for programs and, in collaboration with the analytics department, propose marketing strategies. For example, the marketing department can collect data such as viewer age, gender, and viewing time, and the analytics department can then propose the optimal marketing strategy based on this data. Furthermore, the marketing department can collect viewer feedback in real time and adjust marketing strategies in collaboration with the change response department. This allows the marketing department to provide effective marketing strategies to improve program viewership.

[0065] The integrated management AI agent system can also include a legal department. The legal department can collect legal data related to program production and, in cooperation with the analysis department, assess legal risks. For example, the legal department can collect data such as contract details, copyright status, and labor laws, and the analysis department can use this data to assess legal risks. Furthermore, if a legal issue arises, the legal department can work with the change response department to propose legal countermeasures and notify relevant parties. This allows the legal department to manage the legal risks of program production and prevent legal troubles from occurring.

[0066] The integrated management AI agent system can also include a training department. This training department can provide training programs to system users and instruct them on how to use the system effectively. For example, the training department can provide training to teach new staff the basic operation of the system. It can also provide training explaining how to use new features following system updates. Furthermore, the training department can provide customized training tailored to the user's skill level. This allows the training department to help system users get the most out of the system.

[0067] The integrated management AI agent system can also include a data security department. This department can ensure the security of data within the system and implement measures to prevent unauthorized access and data breaches. For example, the data security department can implement security measures such as data encryption, access control, and the introduction of monitoring systems. Furthermore, the data security department can conduct regular security audits to detect system vulnerabilities and propose corrective actions. In addition, the data security department can respond quickly to security incidents and implement measures to minimize damage. This allows the data security department to enhance the security of data within the system.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects the data necessary for program production. For example, it obtains cast schedules from an online schedule management system, location availability from an online booking system, and staff schedules from a project management tool. This allows for the collection of necessary data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it proposes the optimal filming schedule considering the cast's schedules and the availability of filming locations, suggests the best cast by evaluating the popularity of the cast, and suggests the best filming locations considering the available time for filming. Step 3: The proposal unit proposes the optimal schedule, cast, and filming locations based on the analysis results obtained by the analysis unit. For example, it proposes the optimal filming schedule considering the cast's schedules and the availability of filming locations. Step 4: The Change Response Department will respond to any changes that occur based on the proposals made by the Proposal Department. For example, if the cast's schedule changes, the department will propose a new schedule and notify the relevant parties. Step 5: The sponsor coordination department coordinates with sponsors. For example, they adjust program content and schedules based on sponsor requests.

[0070] (Example of form 2) The integrated management AI agent system according to an embodiment of the present invention is a system that streamlines the entire process of program production. This integrated management AI agent system reduces the burden of communication and coordination among staff by integrating schedule adjustment, casting support, location search and booking into a single AI agent. The integrated management AI agent system collects various data necessary for program production. For example, it collects data such as cast schedules, location availability, and staff schedules. Next, the AI ​​analyzes the collected data and proposes the optimal schedule, cast, and location. For example, it proposes the optimal filming schedule considering the cast's schedule and location availability. Furthermore, the integrated management AI agent system also responds when changes occur. For example, if a cast member's schedule changes, the AI ​​automatically proposes a new schedule and notifies the relevant parties. This reduces the burden on each person in charge and allows the production process to proceed smoothly. The integrated management AI agent system also coordinates with sponsors. For example, it adjusts the program content and schedule based on the sponsor's requests. This can improve sponsor satisfaction. In this way, by using the integrated management AI agent system, the entire process of program production can be streamlined and the burden of communication and coordination among staff can be reduced. This will improve the quality of program production. The integrated AI agent system will streamline the entire program production process and reduce the burden of communication and coordination among staff.

[0071] The integrated management AI agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a change response unit, and a sponsor coordination unit. The data collection unit collects data necessary for program production. Data necessary for program production includes, but is not limited to, cast schedules, location availability, and staff schedules. For example, the data collection unit obtains cast schedules from an online schedule management system. The data collection unit can also obtain location availability from an online reservation system. Furthermore, the data collection unit can obtain staff schedules from a project management tool. For example, the data collection unit obtains cast schedules in real time from an online schedule management system. Location availability is automatically obtained from an online reservation system. Staff schedules are updated periodically from a project management tool. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit proposes the optimal schedule, cast, and location based on the collected data. For example, the analysis unit proposes the optimal filming schedule considering the cast schedule and location availability. Furthermore, the analysis unit can evaluate the popularity of the cast based on the collected data and propose the most suitable cast. In addition, the analysis unit can propose the most suitable filming locations by considering the availability of those locations. For example, the analysis unit proposes the optimal filming schedule based on the cast's schedules and the availability of the filming locations. It evaluates the popularity of the cast and proposes the most suitable cast. It proposes the most suitable filming locations by considering the availability of those locations. The proposal unit proposes the optimal schedule, cast, and filming locations based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the optimal filming schedule by considering the cast's schedules and the availability of the filming locations. For example, the proposal unit proposes the optimal filming schedule by considering the cast's schedules and the availability of the filming locations. Furthermore, the proposal unit can propose the optimal filming schedule by considering the cast's schedules and the availability of the filming locations. In addition, the proposal unit can propose the optimal filming schedule based on the cast's schedules and the availability of the filming locations.For example, the proposal department proposes the optimal filming schedule based on the cast's schedules and the availability of filming locations. The change response department responds to any changes that occur based on the proposals made by the proposal department. For example, if a cast member's schedule changes, the change response department proposes a new schedule and notifies the relevant parties. The change response department also proposes a new schedule and notifies the relevant parties if a cast member's schedule changes. Furthermore, the change response department can also propose a new schedule and notify the relevant parties if a cast member's schedule changes. For example, if a cast member's schedule changes, the change response department proposes a new schedule and notifies the relevant parties if a cast member's schedule changes. The sponsor coordination department coordinates with sponsors. The sponsor coordination department can, for example, adjust program content and schedules based on sponsor requests. The sponsor coordination department can, for example, adjust program content and schedules based on sponsor requests. Furthermore, the sponsor coordination department can also adjust program content and schedules based on sponsor requests. For example, the sponsor coordination department can adjust program content and schedules based on sponsor requests. The program content and schedules can be adjusted based on sponsor requests. The program content and schedules can be adjusted based on sponsor requests. As a result, the integrated management AI agent system according to the embodiment can streamline the entire program production process and reduce the communication and coordination burden among staff.

[0072] The data collection department collects data necessary for program production. This data includes, but is not limited to, cast schedules, location availability, and staff schedules. For example, the department might obtain cast schedules from an online scheduling management system. It could also obtain location availability from an online booking system. Furthermore, it could obtain staff schedules from a project management tool. For instance, the department might obtain cast schedules in real time from an online scheduling management system. Location availability is automatically obtained from an online booking system. Staff schedules are regularly updated from a project management tool. The data collection department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data could be stored on a cloud server and made accessible to the analysis and proposal departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection department to collect data efficiently and effectively, improving the overall system performance. Furthermore, the data collection department can leverage AI to automate the data collection process and reduce human error. For example, the AI ​​automatically detects changes in cast schedules and collects the latest information. It also automatically retrieves updated information and reflects it in the system if the availability of filming locations changes. This ensures that the data collection unit always maintains the most up-to-date data, allowing for smooth progress in program production planning.

[0073] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit proposes the optimal schedule, cast, and filming locations based on the collected data. For example, the analysis unit proposes the optimal filming schedule considering the cast's schedule and the availability of filming locations. The analysis unit can also evaluate the popularity of the cast based on the collected data and propose the most suitable cast. Furthermore, the analysis unit can propose the optimal filming locations considering the available time of the locations. For example, the analysis unit proposes the optimal filming schedule based on the cast's schedule and the availability of filming locations. It evaluates the popularity of the cast and proposes the most suitable cast. It proposes the optimal filming locations considering the available time of the locations. The analysis unit uses AI to analyze this data and simulate multiple scenarios to find the most efficient combination of schedule and cast. For example, the AI ​​receives the cast's schedule and the availability of filming locations as input data and calculates the optimal filming schedule. The AI ​​also selects cast members that will attract viewers' attention based on their popularity and past appearances. Furthermore, the AI ​​proposes the optimal filming locations considering the available time of the locations and their accessibility. This allows the analysis unit to quickly and accurately analyze collected data, maximizing the efficiency of program production. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, it can analyze past filming schedules and fluctuations in cast popularity to help plan future program productions. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early problem detection. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0074] The proposal department proposes the optimal schedule, cast, and filming locations based on the analysis results obtained by the analysis department. For example, the proposal department proposes the optimal filming schedule by considering the cast's schedules and the availability of filming locations. The proposal department proposes the optimal filming schedule based on the cast's schedules and the availability of filming locations. The proposal department can also propose the optimal filming schedule by considering the cast's schedules and the availability of filming locations. Furthermore, the proposal department can also propose the optimal filming schedule based on the cast's schedules and the availability of filming locations. For example, the proposal department proposes the optimal filming schedule by considering the cast's schedules and the availability of filming locations. The proposal department proposes the optimal filming schedule by considering the cast's schedules and the availability of filming locations. The proposal department uses AI to make optimal proposals based on the data provided by the analysis department. For example, the AI ​​receives the cast's schedules and the availability of filming locations as input data and calculates the optimal filming schedule. The AI ​​also selects cast members that will attract viewers' attention based on their popularity and past appearances. Furthermore, the AI ​​suggests optimal filming locations, taking into account factors such as availability and accessibility. This allows the suggestion department to quickly and accurately analyze collected data, maximizing the efficiency of program production. In addition, the suggestion department can utilize historical data and statistics to conduct long-term trend analysis and risk assessment. For example, it can analyze past filming schedules and fluctuations in cast popularity to help plan future program productions. The suggestion department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early problem identification. This allows the suggestion department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0075] The Change Response Department will respond to changes based on the proposals made by the Proposal Department. For example, if a cast member's schedule changes, the Change Response Department will propose a new schedule and notify the relevant parties. The Change Response Department can also propose a new schedule and notify the relevant parties if a cast member's schedule changes. Furthermore, the Change Response Department can also propose a new schedule and notify the relevant parties if a cast member's schedule changes. For example, if a cast member's schedule changes, the Change Response Department will propose a new schedule and notify the relevant parties. If a cast member's schedule changes, the Change Response Department will propose a new schedule and notify the relevant parties. If a cast member's schedule changes, the Change Response Department will propose a new schedule and notify the relevant parties. If a cast member's schedule changes, the Change Response Department will propose a new schedule and notify the relevant parties. The Change Response Department will use AI to respond quickly when changes occur. For example, the AI ​​will automatically detect changes in a cast member's schedule and calculate a new schedule. The AI ​​will also automatically send notifications to relevant parties to inform them of the schedule change. Furthermore, the AI ​​will evaluate the impact of the change and make necessary adjustments. For example, if a cast schedule change affects location bookings, the AI ​​checks the availability of new locations and suggests the most suitable ones. This allows the change management department to respond to changes quickly and accurately, supporting the smooth progress of program production. Furthermore, the change management department can analyze past change history and predict future change risks. For example, it can analyze patterns of past schedule changes and assess the change risks for specific cast members or locations. This allows the change management department to identify risks in advance and take preventative measures.

[0076] The Sponsor Coordination Department handles coordination with sponsors. For example, the Sponsor Coordination Department adjusts program content and schedules based on sponsor requests. The Sponsor Coordination Department can also adjust program content and schedules based on sponsor requests. Furthermore, the Sponsor Coordination Department can also adjust program content and schedules based on sponsor requests. For example, the Sponsor Coordination Department adjusts program content and schedules based on sponsor requests. The Sponsor Coordination Department uses AI to efficiently manage sponsor requests and reflect them in program production. For example, AI automatically analyzes sponsor requests and applies them to program content and schedules. AI also simulates multiple scenarios based on sponsor requests and proposes the optimal adjustment plan. Furthermore, AI automates communication with sponsors and responds quickly to changes or additions in requests. This allows the Sponsor Coordination Department to accurately reflect sponsor requests and improve the efficiency of program production. Furthermore, the sponsor coordination department can analyze past sponsor requests and predict future requests. For example, by analyzing patterns in past sponsor requests, they can understand the program content and schedules preferred by specific sponsors. This allows the sponsor coordination department to predict sponsor requests in advance and reflect them in program production plans.

[0077] The data collection unit can collect data on cast schedules, location availability, and staff schedules. For example, the data collection unit can obtain cast schedules from an online schedule management system. The data collection unit can also obtain location availability from an online booking system. The data collection unit can also obtain staff schedules from a project management tool. This allows for the efficient collection of data necessary for program production. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can obtain cast schedules in real time from an online schedule management system and analyze the data using AI.

[0078] The analysis unit can analyze the collected data and propose appropriate schedules, casts, and filming locations. For example, the analysis unit can propose the optimal schedule, cast, and filming locations based on the collected data. For example, the analysis unit can propose the optimal filming schedule by considering the cast's schedules and the availability of filming locations. The analysis unit can also evaluate the popularity of the cast based on the collected data and propose the most suitable cast. Furthermore, the analysis unit can propose the most suitable filming locations by considering the available time for filming locations. In this way, by analyzing the collected data, the optimal schedule, cast, and filming locations can be proposed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can propose the optimal schedule, cast, and filming locations.

[0079] The proposal unit can propose an appropriate filming schedule based on the cast's schedules and the availability of filming locations. The proposal unit can, for example, propose the optimal filming schedule by considering the cast's schedules and the availability of filming locations. The proposal unit can, for example, propose the optimal filming schedule based on the cast's schedules and the availability of filming locations. The proposal unit can also propose the optimal filming schedule by considering the cast's schedules and the availability of filming locations. This allows the proposal unit to propose the optimal filming schedule by considering the cast's schedules and the availability of filming locations. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the cast's schedules and the availability of filming locations into AI, and the AI ​​can propose the optimal filming schedule.

[0080] The change response unit can propose a new schedule and notify relevant parties when a cast member's schedule changes. For example, the change response unit can propose a new schedule and notify relevant parties when a cast member's schedule changes. The change response unit can also propose a new schedule and notify relevant parties when a cast member's schedule changes. This allows for a quick response when a cast member's schedule changes. Some or all of the above processing in the change response unit may be performed using AI, for example, or not using AI. For example, the change response unit can input information about changes in the cast member's schedule into AI, which can then propose a new schedule and notify relevant parties.

[0081] The sponsor coordination unit can adjust program content and schedules based on sponsor requests. The sponsor coordination unit can, for example, adjust program content and schedules based on sponsor requests. The sponsor coordination unit can, for example, adjust program content and schedules based on sponsor requests. The sponsor coordination unit can also adjust program content and schedules based on sponsor requests. This allows for adjustment of program content and schedules based on sponsor requests. Some or all of the above-described processes in the sponsor coordination unit may be performed using AI, for example, or without AI. For example, the sponsor coordination unit can input sponsor requests into AI, and the AI ​​can adjust program content and schedules.

[0082] 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 reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data and process it quickly. This allows the timing of data collection to be adjusted according to the user's 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, for example, or without AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of data collection.

[0083] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and apply that method. For example, the data collection unit can analyze past data collection history and select the optimal timing to avoid data duplication. For example, the data collection unit can adjust the types of data collected during specific time periods based on past data collection history. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, and the AI ​​can select the optimal collection method.

[0084] The data collection unit can filter data based on the current project and areas of interest during data collection. For example, the data collection unit can collect only data related to the current project and eliminate unnecessary data. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's areas of interest. For example, the data collection unit can dynamically change the types of data required according to the progress of the project. This allows for the priority collection of highly relevant data based on the current project and areas of interest. 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 information about the current project and areas of interest into the AI, which can then filter the data collection.

[0085] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority data. If the user is relaxed, the data collection unit can collect detailed data to improve the accuracy of the analysis. If the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This allows the priority of data to be collected to be determined according to the user's 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, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and determine the priority of data to be collected by the AI.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of nearby filming location information based on the current location. For example, the data collection unit can collect cast schedules related to a specific region based on geographical location information. For example, the data collection unit can collect data on optimal travel routes by considering geographical location information. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0087] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze trends on social media and collect the schedules of relevant cast members. For example, the data collection unit can collect information on popular filming locations based on social media activity. For example, the data collection unit can analyze the interests of users on social media and prioritize the collection of relevant data. This allows for the priority collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into AI, and the AI ​​can collect relevant data.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easily understandable presentation. For example, if the user is relaxed, the analysis unit can provide a presentation that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a concise presentation that gets straight to the point. This allows the presentation of the analysis to be adjusted according to the user's 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the presentation of the analysis.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows the level of detail of the analysis to be adjusted based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a schedule optimization algorithm to cast schedule data. For example, the analysis unit can apply a location availability prediction algorithm to location availability data. For example, the analysis unit can apply a schedule adjustment algorithm to staff schedule data. This allows the appropriate analysis algorithm to be applied depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows the length of the analysis to be adjusted according to the user's 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the length of the analysis.

[0092] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of data submitted earlier. For example, the analysis unit may postpone the analysis of data submitted later. For example, the analysis unit may adjust the analysis schedule based on the submission date. This allows the analysis priority to be determined based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into the AI, and the AI ​​can determine the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically change the order of analysis based on the relevance of the data. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0094] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily visible presentation. For example, if the user is relaxed, the suggestion unit can provide a presentation that includes detailed information. For example, if the user is in a hurry, the suggestion unit can provide a concise presentation that gets straight to the point. This allows the presentation of the suggestion to be adjusted according to the user's 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into AI, and the AI ​​can adjust the way the suggestion is presented.

[0095] The proposal unit can adjust the level of detail of its proposals based on the importance of the data. For example, the proposal unit can provide detailed proposals for highly important data, or simplified proposals for less important data. The proposal unit can also prioritize proposals based on the importance of the data, thereby adjusting the level of detail of the proposals based on the importance of the data. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance of the data into the AI, which can then adjust the level of detail of the proposals.

[0096] The proposal unit can apply different proposal algorithms depending on the data category when making a proposal. For example, the proposal unit can apply a schedule optimization algorithm to cast schedule data. For example, the proposal unit can apply an availability prediction algorithm to location availability data. For example, the proposal unit can apply a schedule adjustment algorithm to staff schedule data. This allows the appropriate proposal algorithm to be applied depending on the data category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the data category into the AI, and the AI ​​can apply an appropriate proposal algorithm.

[0097] 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 detailed suggestion. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This allows the length of the suggestion to be adjusted according to 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the length of the suggestion.

[0098] The proposal department can determine the priority of proposals based on the data submission timing. For example, the proposal department may prioritize proposals for data submitted earlier. For example, the proposal department may postpone proposals for data submitted later. The proposal department may adjust the proposal schedule based on the submission timing. This allows the proposal priority to be determined based on the data submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the data submission timing into the AI, and the AI ​​can determine the proposal priority.

[0099] The proposal unit can adjust the order of proposals based on the relevance of the data. For example, the proposal unit can prioritize proposals for highly relevant data. For example, the proposal unit can postpone proposals for less relevant data. For example, the proposal unit can dynamically change the order of proposals based on the relevance of the data. This allows the order of proposals to be adjusted based on the relevance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of proposals.

[0100] The change response unit can estimate the user's emotions and adjust the change response method based on the estimated user emotions. For example, if the user is stressed, the change response unit can provide a simple and quick response method. For example, if the user is relaxed, the change response unit can provide a response method that includes a detailed explanation. For example, if the user is in a hurry, the change response unit can provide a quick response method. This allows the change response method to be adjusted according to the user's 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 change response unit may be performed using AI, for example, or not using AI. For example, the change response unit can input user emotion data into AI, and the AI ​​can adjust the change response method.

[0101] The change response unit can analyze past change history to select the optimal response method when responding to a change. For example, the change response unit can identify the most effective response method from past change history and apply that method. For example, the change response unit can analyze past change history and select the optimal response procedure for similar changes. For example, the change response unit can customize response methods to suit specific situations based on past change history. This allows the optimal response method to be selected by analyzing past change history. Some or all of the above processes in the change response unit may be performed using AI, for example, or without AI. For example, the change response unit can input past change history into AI, and the AI ​​can select the optimal response method.

[0102] The change response unit can customize the response measures based on the current project status when a change is addressed. For example, the change response unit can select the optimal response measures according to the current project progress. For example, the change response unit can adjust the response measures based on project priorities. For example, the change response unit can customize the response measures considering the current project resource situation. This allows the response measures to be customized based on the current project status. Some or all of the above processes in the change response unit may be performed using AI, for example, or without AI. For example, the change response unit can input the current project status into the AI, and the AI ​​can customize the response measures.

[0103] The change response unit can estimate the user's emotions and determine the priority of change responses based on the estimated emotions. For example, if the user is stressed, the change response unit will prioritize high-priority changes. If the user is relaxed, the change response unit can perform detailed change responses. If the user is in a hurry, the change response unit can prioritize changes that can be addressed quickly. This allows the priority of change responses to be determined according to the user's 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 change response unit may be performed using AI or not using AI. For example, the change response unit can input user emotion data into an AI, which can then determine the priority of change responses.

[0104] The change response unit can select the optimal response method when responding to a change, taking geographical location information into consideration. For example, the change response unit can select a response method that utilizes nearby resources based on the current location. For example, the change response unit can select a response method related to a specific region based on geographical location information. For example, the change response unit can select a response method regarding the optimal travel route, taking geographical location information into consideration. This allows for the selection of the optimal response method by taking geographical location information into consideration. Some or all of the above-described processes in the change response unit may be performed using AI, for example, or without using AI. For example, the change response unit can input geographical location information into AI, and the AI ​​can select the optimal response method.

[0105] The change response unit can analyze social media activity and propose response measures when responding to changes. For example, the change response unit can analyze trends on social media and propose relevant response measures. For example, the change response unit can propose popular response measures based on social media activity. For example, the change response unit can analyze user interests on social media and preferentially propose relevant response measures. In this way, relevant response measures can be proposed by analyzing social media activity. Some or all of the above processing in the change response unit may be performed using AI, for example, or without AI. For example, the change response unit can input social media activity data into AI, and the AI ​​can propose relevant response measures.

[0106] The sponsor adjustment unit can estimate the user's emotions and adjust the sponsor adjustment method based on the estimated user emotions. For example, if the user is stressed, the sponsor adjustment unit can provide a simple and quick adjustment method. For example, if the user is relaxed, the sponsor adjustment unit can provide an adjustment method that includes a detailed explanation. For example, if the user is in a hurry, the sponsor adjustment unit can provide a method that allows for quick adjustment. This allows the sponsor adjustment method to be adjusted according to the user's 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 sponsor adjustment unit may be performed using AI or not using AI. For example, the sponsor adjustment unit can input user emotion data into AI, and the AI ​​can adjust the sponsor adjustment method.

[0107] The sponsor adjustment unit can analyze past sponsor history to select the optimal adjustment method during sponsor adjustment. For example, the sponsor adjustment unit can identify the most effective adjustment method from past sponsor history and apply that method. For example, the sponsor adjustment unit can analyze past sponsor history and select the optimal adjustment procedure for similar sponsors. For example, the sponsor adjustment unit can customize the adjustment method to suit a specific situation based on past sponsor history. This allows the optimal adjustment method to be selected by analyzing past sponsor history. Some or all of the above processes in the sponsor adjustment unit may be performed using AI, for example, or without AI. For example, the sponsor adjustment unit can input past sponsor history into AI, and the AI ​​can select the optimal adjustment method.

[0108] The sponsor coordination unit can customize the coordination means based on the current project status when coordinating sponsors. For example, the sponsor coordination unit can select the optimal coordination means according to the current project progress. For example, the sponsor coordination unit can adjust the coordination means based on project priority. For example, the sponsor coordination unit can customize the coordination means considering the current project resource status. This allows the coordination means to be customized based on the current project status. Some or all of the above processes in the sponsor coordination unit may be performed using AI, for example, or without AI. For example, the sponsor coordination unit can input the current project status into AI, and the AI ​​can customize the coordination means.

[0109] The sponsor coordination unit can estimate the user's emotions and determine the priority of sponsor adjustments based on the estimated user emotions. For example, if the user is stressed, the sponsor coordination unit will prioritize high-priority adjustments. For example, if the user is relaxed, the sponsor coordination unit can perform detailed adjustments. For example, if the user is in a hurry, the sponsor coordination unit can prioritize adjustments that can be handled quickly. This allows the priority of sponsor adjustments to be determined according to the user's 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 sponsor coordination unit may be performed using AI or not using AI. For example, the sponsor coordination unit can input user emotion data into an AI, and the AI ​​can determine the priority of sponsor adjustments.

[0110] The sponsor coordination unit can select the optimal coordination method when coordinating sponsors, taking geographical location information into consideration. For example, the sponsor coordination unit can select a coordination method that utilizes nearby resources based on the current location. For example, the sponsor coordination unit can select coordination means related to a specific region based on geographical location information. For example, the sponsor coordination unit can select a coordination method for the optimal travel route, taking geographical location information into consideration. This allows for the selection of the optimal coordination method, taking geographical location information into consideration. Some or all of the above processing in the sponsor coordination unit may be performed using AI, for example, or without AI. For example, the sponsor coordination unit can input geographical location information into AI, and the AI ​​can select the optimal coordination method.

[0111] The sponsor coordination unit can analyze social media activity and propose coordination measures when coordinating sponsorships. For example, the sponsor coordination unit can analyze trends on social media and propose relevant coordination measures. For example, the sponsor coordination unit can propose popular coordination measures based on social media activity. For example, the sponsor coordination unit can analyze user interests on social media and preferentially propose relevant coordination measures. In this way, relevant coordination measures can be proposed by analyzing social media activity. Some or all of the above processes in the sponsor coordination unit may be performed using AI, for example, or without AI. For example, the sponsor coordination unit can input social media activity data into AI, and the AI ​​can propose relevant coordination measures.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The integrated management AI agent system can also include a budget management department. This department can collect budget data necessary for program production and, in cooperation with the analysis department, propose the optimal budget allocation. For example, the budget management department collects data such as cast salaries, location usage fees, and staff personnel costs, and the analysis department proposes the optimal budget allocation based on this data. Furthermore, if budget changes occur, the budget management department can work with the change response department to propose a new budget allocation and notify relevant parties. This allows the budget management department to streamline program production budget management and reduce waste.

[0114] The integrated management AI agent system can also include a risk management department. This department can collect risk factors in program production and, in cooperation with the analysis department, conduct risk assessments. For example, the risk management department collects data such as weather information, the health status of the cast, and the safety of filming locations, and the analysis department uses this data to conduct risk assessments. Furthermore, if a risk occurs, the risk management department can work with the change response department to propose risk mitigation measures and notify relevant parties. This allows the risk management department to streamline risk management in program production and minimize the impact of risks.

[0115] The integrated management AI agent system can also include a quality control department. This department can collect quality data during program production and, in cooperation with the analysis department, perform quality evaluations. For example, the quality control department can collect data such as image quality of filmed footage, audio clarity, and editing accuracy, and the analysis department can use this data to perform quality evaluations. Furthermore, if quality issues arise, the quality control department can collaborate with the change response department to propose quality improvement measures and notify relevant parties. This allows the quality control department to streamline quality control in program production and improve program quality.

[0116] The integrated management AI agent system can also include a marketing department. This department can collect viewer data for programs and, in collaboration with the analytics department, propose marketing strategies. For example, the marketing department can collect data such as viewer age, gender, and viewing time, and the analytics department can then propose the optimal marketing strategy based on this data. Furthermore, the marketing department can collect viewer feedback in real time and adjust marketing strategies in collaboration with the change response department. This allows the marketing department to provide effective marketing strategies to improve program viewership.

[0117] The integrated management AI agent system can also include a legal department. The legal department can collect legal data related to program production and, in cooperation with the analysis department, assess legal risks. For example, the legal department can collect data such as contract details, copyright status, and labor laws, and the analysis department can use this data to assess legal risks. Furthermore, if a legal issue arises, the legal department can work with the change response department to propose legal countermeasures and notify relevant parties. This allows the legal department to manage the legal risks of program production and prevent legal troubles from occurring.

[0118] The integrated management AI agent system can also include an emotion estimation unit. This unit can estimate the user's emotions and adjust the suggestions and responses of each unit based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can adjust the suggestion unit to provide simple and quick suggestions. If the user is relaxed, the emotion estimation unit can adjust the analysis unit to provide detailed analysis results. Furthermore, if the user is in a hurry, the emotion estimation unit can adjust the change response unit to provide a way to respond quickly. This allows the emotion estimation unit to optimize the suggestions and responses of the entire system according to the user's emotions.

[0119] The integrated management AI agent system can also include a user feedback unit. This unit collects user feedback and, in cooperation with the analysis unit, proposes improvements to the system. For example, the user feedback unit collects data such as user satisfaction, acceptance rates of suggestions, and ease of use, and the analysis unit uses this data to propose system improvements. Furthermore, the user feedback unit can work with the change response unit to adjust system functions and interfaces based on the feedback. This allows the user feedback unit to implement system improvements that reflect user opinions.

[0120] The integrated management AI agent system can also include a training department. This training department can provide training programs to system users and instruct them on how to use the system effectively. For example, the training department can provide training to teach new staff the basic operation of the system. It can also provide training explaining how to use new features following system updates. Furthermore, the training department can provide customized training tailored to the user's skill level. This allows the training department to help system users get the most out of the system.

[0121] The integrated management AI agent system can also include a data security department. This department can ensure the security of data within the system and implement measures to prevent unauthorized access and data breaches. For example, the data security department can implement security measures such as data encryption, access control, and the introduction of monitoring systems. Furthermore, the data security department can conduct regular security audits to detect system vulnerabilities and propose corrective actions. In addition, the data security department can respond quickly to security incidents and implement measures to minimize damage. This allows the data security department to enhance the security of data within the system.

[0122] The integrated management AI agent system can also include a user support department. This department can respond to inquiries and problems from system users, providing prompt and appropriate support. For example, it can provide clear explanations to users regarding system operation. It can also quickly address and resolve system malfunctions and errors. Furthermore, it can collect user feedback and suggest improvements to the system. In this way, the user support department can help users use the system with confidence.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects the data necessary for program production. For example, it obtains cast schedules from an online schedule management system, location availability from an online booking system, and staff schedules from a project management tool. This allows for the collection of necessary data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it proposes the optimal filming schedule considering the cast's schedules and the availability of filming locations, suggests the best cast by evaluating the popularity of the cast, and suggests the best filming locations considering the available time for filming. Step 3: The proposal unit proposes the optimal schedule, cast, and filming locations based on the analysis results obtained by the analysis unit. For example, it proposes the optimal filming schedule considering the cast's schedules and the availability of filming locations. Step 4: The Change Response Department will respond to any changes that occur based on the proposals made by the Proposal Department. For example, if the cast's schedule changes, the department will propose a new schedule and notify the relevant parties. Step 5: The sponsor coordination department coordinates with sponsors. For example, they adjust program content and schedules based on sponsor requests.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, change response unit, and sponsor coordination unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and acquires data from an online schedule management system or an online reservation system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose the optimal schedule, cast, and filming locations. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes the optimal proposal based on the analysis results. The change response unit is implemented by the control unit 46A of the smart device 14 and proposes a new schedule and notifies the relevant parties when changes occur. The sponsor coordination unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the program content and schedule based on the sponsor's requests. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[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 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[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 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.

[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 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.

[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 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.

[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 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, change response unit, and sponsor coordination unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and acquires data from an online schedule management system or an online reservation system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose the optimal schedule, cast, and filming locations. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes the optimal proposal based on the analysis results. The change response unit is implemented by the control unit 46A of the smart glasses 214 and proposes a new schedule and notifies the relevant parties when changes occur. The sponsor coordination unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the program content and schedule based on the sponsor's requests. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[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 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[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 (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).

[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] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, change response unit, and sponsor coordination unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and acquires data from an online schedule management system or an online reservation system. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose the optimal schedule, cast, and filming locations. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes the optimal proposal based on the analysis results. The change response unit is implemented by, for example, the control unit 46A of the headset terminal 314 and proposes a new schedule and notifies the relevant parties when changes occur. The sponsor coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts the program content and schedule based on the sponsor's requests. The correspondence between each unit and the devices and control units is not limited to the examples described above and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, change response unit, and sponsor coordination unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and acquires data from an online schedule management system or an online reservation system. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose the optimal schedule, cast, and filming locations. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes the optimal proposal based on the analysis results. The change response unit is implemented by, for example, the control unit 46A of the robot 414 and proposes a new schedule and notifies the relevant parties when changes occur. The sponsor coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts the program content and schedule based on the sponsor's requests. The correspondence between each unit and the devices and control units is not limited to the examples described above and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The data collection department collects the data necessary for program production, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an appropriate schedule, cast, and filming locations. A change handling unit is provided to respond when changes occur based on the content proposed by the aforementioned proposal unit, It includes a sponsor coordination department that handles coordination with sponsors. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data on cast schedules, location availability, and staff schedules. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data and propose appropriate schedules, casts, and filming locations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the cast's schedules and the availability of filming locations, we will propose a suitable filming schedule. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned change handling unit is: If the cast's schedule changes, propose a new schedule and notify those involved. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned sponsor coordination unit, The program content and schedule are adjusted based on the sponsor's requests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection 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 8) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filter it based on the current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection 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 11) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned proposal section is, When submitting a proposal, we will prioritize the proposals based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned change handling unit is: We estimate the user's emotions and adjust how we respond to changes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned change handling unit is: When implementing changes, analyze past change history to select the most appropriate solution. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned change handling unit is: When implementing changes, customize the response method based on the current project status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned change handling unit is: It estimates user sentiment and determines the priority of changes based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned change handling unit is: When implementing changes, the most appropriate response method will be selected, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned change handling unit is: When implementing changes, we analyze social media activity and propose appropriate response measures. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned sponsor coordination unit, We estimate user sentiment and adjust sponsorship strategies based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned sponsor coordination unit, When negotiating sponsorships, we analyze past sponsorship history to select the most suitable negotiation method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned sponsor coordination unit, When coordinating sponsorships, customize the coordination methods based on the current project status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned sponsor coordination unit, The system estimates user sentiment and prioritizes sponsor adjustments based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned sponsor coordination unit, When coordinating sponsorships, the optimal coordination method is selected by considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned sponsor coordination unit, When negotiating sponsorships, we analyze social media activity and propose negotiation strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 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 department collects the data necessary for program production, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an appropriate schedule, cast, and filming locations. A change handling unit is provided to respond when changes occur based on the content proposed by the aforementioned proposal unit, It includes a sponsor coordination department that handles coordination with sponsors. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on cast schedules, location availability, and staff schedules. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected data and propose appropriate schedules, casts, and filming locations. The system according to feature 1.

4. The aforementioned proposal section is, Based on the cast's schedules and the availability of filming locations, we will propose a suitable filming schedule. The system according to feature 1.

5. The aforementioned change handling unit is: If the cast's schedule changes, propose a new schedule and notify those involved. The system according to feature 1.

6. The aforementioned sponsor coordination unit, The program content and schedule are adjusted based on the sponsor's requests. The system according to feature 1.

7. The aforementioned collection 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.

8. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

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