system

Abstract

The system according to this embodiment aims to optimize the schedules of team members and achieve equal coordination. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an optimization unit, and a provision unit. The collection unit collects schedule data. The analysis unit analyzes the data collected by the collection unit. The optimization unit optimizes the schedule based on the analysis results obtained by the analysis unit. The provision unit provides the schedule optimized by the optimization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is laborious to adjust the schedules of members within a team and it is difficult to achieve an equal adjustment.

[0005] The system according to the embodiment aims to optimize the schedules of members within a team and achieve an equal adjustment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an optimization unit, and a data provision unit. The data collection unit collects schedule data. The analysis unit analyzes the data collected by the data collection unit. The optimization unit optimizes the schedule based on the analysis results obtained by the analysis unit. The data provision unit provides the schedule optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize the schedules of team members and achieve equal coordination. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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) An AI agent schedule optimizer according to an embodiment of the present invention is a system that optimizes the schedules of members within an organization or team. In this system, users request schedule optimization via chat or voice input, and the AI ​​agent collects and analyzes each member's schedule data to provide an optimal schedule. This streamlines the meeting scheduling process and frees up members' working time. Furthermore, the AI ​​agent addresses complaints and dissatisfaction, ensuring fair scheduling. For example, the AI ​​agent schedule optimizer receives a request for schedule optimization via chat or voice input. For instance, if a user requests, "Please adjust next week's meeting," the AI ​​agent collects each member's schedule data. Next, the AI ​​agent analyzes the collected data to find common free time slots. For example, the AI ​​agent analyzes each member's calendar to identify time slots when everyone is free. Then, the AI ​​agent optimizes the schedule based on the analysis results. For example, the AI ​​agent proposes the optimal meeting time and notifies each member. Finally, the AI ​​agent provides the optimized schedule. For example, the AI ​​agent automatically adds the optimized schedule to each member's calendar. This streamlines the meeting scheduling process, freeing up members' work time. Furthermore, the AI ​​agent has the ability to address complaints and grievances. For example, if a member is dissatisfied with their schedule, the AI ​​agent will acknowledge their concerns and take appropriate action. This ensures fair scheduling. As a result, the AI ​​agent schedule optimizer can efficiently optimize the schedules of members within an organization or team, achieving fair scheduling.

[0029] The AI ​​agent schedule optimizer according to this embodiment comprises a collection unit, an analysis unit, an optimization unit, and a provision unit. The collection unit collects schedule data. The collection unit can, for example, obtain schedule data from each member's calendar. The collection unit can, for example, automatically collect schedule data in cooperation with each member's calendar application. The collection unit can, for example, obtain schedule data from each member's calendar application via an API. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, perform statistical analysis on the collected schedule data. The analysis unit can, for example, analyze the collected schedule data using pattern recognition technology. The analysis unit can, for example, analyze the collected schedule data using machine learning algorithms. The optimization unit optimizes the schedule based on the analysis results obtained by the analysis unit. The optimization unit can, for example, improve the efficiency of the schedule based on the analysis results. The optimization unit can, for example, optimize resource allocation based on the analysis results. The optimization unit can, for example, set schedule priorities based on the analysis results. The provision unit provides the schedule optimized by the optimization unit. The service provider can, for example, notify the user of the optimized schedule. The service provider can, for example, automatically add the optimized schedule to the user's calendar. The service provider can, for example, send the optimized schedule to the user via email. This allows the AI ​​agent schedule optimizer according to the embodiment to efficiently collect, analyze, optimize, and provide schedule data.

[0030] The data collection unit collects schedule data. Specifically, it can automatically collect schedule data by linking with each member's calendar application. For example, the data collection unit obtains schedule data from each member's calendar application via an API. In this process, the data collection unit provides appropriate authentication information to each member's calendar application and obtains the necessary access permissions. This allows the data collection unit to obtain each member's schedule data in real time and store it in a central database. Furthermore, the data collection unit can flexibly set the frequency and timing of schedule data collection. For example, it can be set to collect schedule data at a fixed time every day, or to collect data immediately when there is a change in the schedule. This ensures that the data collection unit always maintains the latest schedule data, making it available to the analysis and optimization units. In addition, the data collection unit has a checking function to ensure data integrity and consistency when collecting schedule data. For example, it can detect duplicate schedules and inconsistent data and process them appropriately. This allows the data collection unit to provide accurate and reliable schedule data.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it performs statistical analysis on the collected schedule data to understand each member's schedule patterns and trends. For example, the analysis unit can identify busy and free time slots based on each member's schedule data, and use this information to improve schedule efficiency. The analysis unit can also use pattern recognition technology to find specific patterns and regularities within the schedule data. For example, it can detect patterns of regularly repeated meetings and events and optimize schedules based on these patterns. Furthermore, the analysis unit can use machine learning algorithms to analyze schedule data. For example, it can learn from past schedule data to help predict and optimize future schedules. Machine learning algorithms can extract important features from the schedule data and use them to improve schedule efficiency and optimize resource allocation. In this way, the analysis unit can analyze the collected schedule data from multiple perspectives and provide the information necessary for schedule optimization.

[0032] The optimization unit optimizes the schedule based on the analysis results obtained by the analysis unit. Specifically, it can improve the efficiency of the schedule based on the analysis results. For example, it optimizes meeting and event schedules by considering each member's busy and free times. The optimization unit can also optimize resource allocation. For example, it can optimize project resources based on each member's schedule to achieve efficient work. Furthermore, the optimization unit can set schedule priorities. For example, it can prioritize scheduling high-priority tasks and meetings to achieve efficient schedule management. The optimization unit can perform these optimization processes automatically and provide the user with an optimized schedule. In this way, the optimization unit can improve the efficiency of the schedule and optimize resource allocation, thereby improving the user's work efficiency.

[0033] The service provider provides the optimized schedule, which has been optimized by the optimization unit. Specifically, it can notify users of the optimized schedule. For example, the service provider can automatically add the optimized schedule to the user's calendar. This allows the user to check the optimized schedule and make changes or adjustments to it. The service provider can also send the optimized schedule to the user via email. This allows the user to make changes or adjustments to the schedule via email. Furthermore, the service provider can notify the user of the optimized schedule on their smartphone. This allows the user to make changes or adjustments to the schedule via their smartphone. Through these notification functions, the service provider can quickly provide users with optimized schedules and support the efficiency of their schedules. In this way, the service provider can streamline the user's schedule management and improve work efficiency.

[0034] The receiving unit can receive complaints and grievances. For example, the receiving unit can receive complaints and grievances from users via chat. For example, the receiving unit can receive complaints and grievances from users via voice input. For example, the receiving unit can receive complaints and grievances from users via email. In this way, the receiving unit can improve user satisfaction by receiving complaints and grievances. Some or all of the above processing in the receiving unit may be performed using, for example, a generating AI, or without a generating AI. For example, the receiving unit can input complaints and grievances from users into a generating AI, and the generating AI can generate an appropriate response.

[0035] The adjustment unit can adjust the schedule. For example, the adjustment unit can receive schedule change requests from users. For example, the adjustment unit can readjust the schedule based on schedule change requests from users. For example, the adjustment unit can propose an optimal schedule again based on schedule change requests from users. This allows the adjustment unit to adjust the schedule efficiently. Some or all of the above processes in the adjustment unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the adjustment unit can input a schedule change request from a user into a generation AI, and the generation AI can propose an optimal schedule again.

[0036] The reception desk can receive instructions from users. For example, the reception desk can receive schedule optimization requests from users via chat. For example, the reception desk can receive schedule optimization requests from users via voice input. For example, the reception desk can receive schedule optimization requests from users via email. This allows the reception desk to efficiently receive instructions from users. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input a schedule optimization request from a user into a generative AI, and the generative AI can generate an appropriate response.

[0037] The data collection unit can collect schedule data from each member. The data collection unit can, for example, obtain schedule data from each member's calendar. The data collection unit can, for example, automatically collect schedule data by coordinating with each member's calendar application. The data collection unit can, for example, obtain schedule data from each member's calendar application via an API. This allows the data collection unit to efficiently collect schedule data from each member. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the schedule data obtained from each member's calendar application into a generative AI, which can then analyze the data.

[0038] The analysis unit can analyze the collected data and find common free time. For example, the analysis unit can statistically analyze the collected schedule data to identify common free time. For example, the analysis unit can analyze the collected schedule data using pattern recognition technology to find common free time. For example, the analysis unit can analyze the collected schedule data using machine learning algorithms to find common free time. This allows the analysis unit to efficiently find common free time. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected schedule data into a generative AI, which can then identify common free time.

[0039] The optimization unit can optimize the schedule based on the analysis results. For example, the optimization unit can improve the efficiency of the schedule based on the analysis results. For example, the optimization unit can optimize the allocation of resources based on the analysis results. For example, the optimization unit can set schedule priorities based on the analysis results. This allows the optimization unit to efficiently optimize the schedule. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the optimization unit can input the analysis results into a generation AI, which can then generate an optimal schedule.

[0040] The service provider can provide an optimized schedule. The service provider can, for example, notify the user of the optimized schedule. The service provider can, for example, automatically add the optimized schedule to the user's calendar. The service provider can, for example, send the optimized schedule to the user via email. This allows the service provider to efficiently provide the optimized schedule. Some or all of the above processes in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the optimized schedule into a generative AI, and the generative AI can generate an appropriate notification method.

[0041] The data collection unit can analyze each member's past schedule history and select the optimal data collection method. For example, the data collection unit can analyze each member's past schedule history and select the most efficient data collection method. For example, the data collection unit can optimize the data collection frequency based on each member's past schedule history. For example, the data collection unit can adjust the data collection timing based on each member's past schedule history. In this way, the data collection unit can select the optimal data collection method by analyzing past schedule history. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input each member's past schedule history into a generating AI, and the generating AI can select the optimal data collection method.

[0042] The data collection unit can filter schedule data based on each member's current projects and areas of interest when collecting it. For example, the data collection unit can prioritize collecting relevant schedule data based on each member's current projects. For example, the data collection unit can filter relevant schedule data based on each member's areas of interest. For example, the data collection unit can collect optimal schedule data by considering each member's current projects and areas of interest. This allows the data collection unit to collect highly relevant data by filtering the data based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input data on each member's current projects and areas of interest into a generative AI, which can then perform optimal filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each member when collecting schedule data. For example, the data collection unit can prioritize the collection of highly relevant schedule data based on the geographical location information of each member. For example, the data collection unit can determine the optimal collection timing by considering the geographical location information of each member. For example, the data collection unit can determine the priority of schedule data to be collected based on the geographical location information of each member. In this way, the data collection unit can efficiently collect highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input the geographical location information of each member into a generating AI, and the generating AI can select the optimal collection method.

[0044] The data collection unit can analyze each member's social media activity and collect relevant data when collecting schedule data. For example, the data collection unit can analyze each member's social media activity and collect relevant schedule data. For example, the data collection unit can determine the priority of schedule data to collect based on each member's social media activity. For example, the data collection unit can select the optimal data collection method considering each member's social media activity. This allows the data collection unit to efficiently collect relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input each member's social media activity data into a generative AI, which can then select the optimal data collection method.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the schedule data during the analysis. For example, the analysis unit can perform a detailed analysis on schedule data with high importance. For example, the analysis unit can perform a simplified analysis on schedule data with low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the schedule data. This allows the analysis unit to perform analysis efficiently by adjusting the level of detail of the analysis according to the importance of the schedule data. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the importance of the schedule data into the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the schedule data during analysis. For example, the analysis unit can apply the optimal analysis algorithm to meeting schedules. For example, the analysis unit can apply different analysis algorithms to project schedules. For example, the analysis unit can select the optimal analysis algorithm depending on the category of the schedule data. This allows the analysis unit to improve the accuracy of the analysis by applying the optimal analysis algorithm according to the category of the schedule data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the category of the schedule data into the generative AI, and the generative AI can select the optimal analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the submission timing of the schedule data during the analysis. For example, the analysis unit may prioritize the analysis of schedule data with an approaching submission deadline. For example, the analysis unit may postpone the analysis of schedule data with a distant submission deadline. The analysis unit can dynamically adjust the priority of analysis based on the submission timing. This allows the analysis unit to perform analysis efficiently by determining the priority of analysis based on the submission timing. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the submission timing of the schedule data into the generating AI, and the generating AI can determine the priority of analysis.

[0048] The analysis unit can adjust the order of analysis based on the relationships between schedule data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant schedule data. For example, the analysis unit may postpone the analysis of less relevant schedule data. The analysis unit can dynamically adjust the order of analysis based on the relationships between schedule data. This allows the analysis unit to perform analysis efficiently by adjusting the order of analysis based on relationships. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the relationships between schedule data into a generating AI, and the generating AI can adjust the order of analysis.

[0049] The optimization unit can improve the accuracy of optimization by considering the interrelationships of schedule data during optimization. For example, the optimization unit analyzes the interrelationships of schedule data and proposes an optimal schedule. The optimization unit can improve the accuracy of optimization by considering the interrelationships of schedule data. For example, the optimization unit can generate an optimal schedule based on the interrelationships of schedule data. In this way, the optimization unit can improve the accuracy of optimization by considering the interrelationships of schedule data. Some or all of the above processing in the optimization unit may be performed using a generation AI, for example, or without a generation AI. For example, the optimization unit can input the interrelationships of schedule data to a generation AI, and the generation AI can generate an optimal schedule.

[0050] The optimization unit can perform optimization while considering the attribute information of each member. For example, the optimization unit can propose an optimal schedule by considering each member's position and job duties. For example, the optimization unit can improve the accuracy of optimization based on each member's attribute information. For example, the optimization unit can generate an optimal schedule by considering each member's attribute information. In this way, the optimization unit can generate an optimal schedule by considering each member's attribute information. Some or all of the above processing in the optimization unit may be performed using a generation AI, for example, or without a generation AI. For example, the optimization unit can input each member's attribute information into a generation AI, and the generation AI can generate an optimal schedule.

[0051] The optimization unit can perform optimization while considering the geographical distribution of schedule data. For example, the optimization unit proposes an optimal schedule based on the geographical distribution of schedule data. For example, the optimization unit can improve the accuracy of optimization by considering the geographical distribution of schedule data. For example, the optimization unit can generate an optimal schedule based on the geographical distribution of schedule data. In this way, the optimization unit can generate an optimal schedule by considering the geographical distribution of schedule data. Some or all of the above processing in the optimization unit may be performed using a generation AI, for example, or without a generation AI. For example, the optimization unit can input the geographical distribution of schedule data to a generation AI, and the generation AI can generate an optimal schedule.

[0052] The optimization unit can improve the accuracy of optimization by referring to relevant literature on the schedule data during optimization. For example, the optimization unit can refer to relevant literature on the schedule data and propose an optimal schedule. For example, the optimization unit can improve the accuracy of optimization based on relevant literature on the schedule data. For example, the optimization unit can generate an optimal schedule by referring to relevant literature on the schedule data. In this way, the optimization unit can improve the accuracy of optimization by referring to relevant literature. Some or all of the above processing in the optimization unit may be performed using a generation AI, for example, or without a generation AI. For example, the optimization unit can input relevant literature on the schedule data into a generation AI, and the generation AI can generate an optimal schedule.

[0053] The information provider can adjust the level of detail provided based on the importance of the schedule at the time of provision. For example, the provider can provide detailed information for schedules of high importance. For example, the provider can provide simplified information for schedules of low importance. The provider can dynamically adjust the level of detail provided according to the importance of the schedule. This allows the provider to efficiently provide information by adjusting the level of detail provided according to the importance of the schedule. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without a generating AI. For example, the provider can input the importance of the schedule into the generating AI, and the generating AI can adjust the level of detail provided.

[0054] The information delivery unit can apply different delivery algorithms depending on the schedule category at the time of delivery. For example, the information delivery unit can apply the optimal delivery algorithm to a meeting schedule. For example, the information delivery unit can apply a different delivery algorithm to a project schedule. For example, the information delivery unit can select the optimal delivery algorithm depending on the schedule category. This allows the information delivery unit to improve the accuracy of information delivery by applying the optimal delivery algorithm according to the schedule category. Some or all of the above processing in the information delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information delivery unit can input the schedule category into a generative AI, and the generative AI can select the optimal delivery algorithm.

[0055] The service provider can determine the priority of service provision based on the submission timing of the schedules. For example, the service provider can prioritize providing schedules with approaching deadlines. For example, the service provider can postpone providing schedules with distant deadlines. The service provider can dynamically adjust the priority of service provision based on the submission timing. This allows the service provider to efficiently provide information by determining the priority of service provision based on the submission timing. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input the submission timing of the schedules into a generating AI, and the generating AI can determine the priority of service provision.

[0056] The service provider can adjust the order of delivery based on the relevance of the schedules at the time of delivery. For example, the service provider can prioritize the delivery of highly relevant schedules. For example, the service provider can postpone less relevant schedules. The service provider can dynamically adjust the order of delivery based on the relevance of the schedules. This allows the service provider to efficiently deliver information by adjusting the order of delivery based on relevance. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the relevance of the schedules into a generative AI, and the generative AI can adjust the order of delivery.

[0057] The receiving unit can select the optimal receiving method by referring to past complaint data when receiving feedback. For example, the receiving unit can refer to past complaint data and select the optimal receiving method. For example, the receiving unit can adjust the receiving method based on past complaint data. For example, the receiving unit can analyze past complaint data and propose the optimal receiving method. In this way, the receiving unit can select the optimal receiving method by referring to past complaint data. Some or all of the above processing in the receiving unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the receiving unit can input past complaint data into a generating AI, and the generating AI can select the optimal receiving method.

[0058] The receiving unit can select the optimal receiving method when receiving data, taking into account the user's geographical location information. For example, the receiving unit can select the optimal receiving method based on the user's geographical location information. For example, the receiving unit can adjust the receiving method, taking into account the user's geographical location information. For example, the receiving unit can determine the priority of receiving data based on the user's geographical location information. In this way, the receiving unit can select the optimal receiving method by taking into account geographical location information. Some or all of the above processing in the receiving unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the receiving unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal receiving method.

[0059] The adjustment unit can select the optimal adjustment method by referring to past adjustment data during adjustment. For example, the adjustment unit can refer to past adjustment data and select the optimal adjustment method. For example, the adjustment unit can adjust the adjustment method based on past adjustment data. For example, the adjustment unit can analyze past adjustment data and propose the optimal adjustment method. In this way, the adjustment unit can select the optimal adjustment method by referring to past adjustment data. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit can input past adjustment data into a generating AI, and the generating AI can select the optimal adjustment method.

[0060] The adjustment unit can select the optimal adjustment method during adjustment, taking into account the user's geographical location information. For example, the adjustment unit selects the optimal adjustment method based on the user's geographical location information. For example, the adjustment unit can adjust the adjustment method, taking into account the user's geographical location information. For example, the adjustment unit can determine the priority of adjustments based on the user's geographical location information. In this way, the adjustment unit can select the optimal adjustment method by taking geographical location information into account. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal adjustment method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The analysis unit can perform analysis of schedule data while considering each member's past performance data. For example, it can propose an optimal schedule for each member based on past performance data. It can analyze past performance data to improve schedule efficiency. It can set schedule priorities by referring to past performance data. As a result, the analysis unit can perform more accurate analysis by considering past performance data.

[0063] The optimization unit can optimize schedule data while considering each member's health data. For example, it can propose a schedule that reduces the burden on each member based on their health data. It can analyze health data and generate an optimal schedule according to their health status. It can also set schedule priorities by referring to health data. In this way, the optimization unit can provide an optimal schedule that takes each member's health status into consideration by taking health data into account.

[0064] The service provider can adjust the method of providing schedules according to each member's position and job responsibilities. For example, they can provide detailed schedules to members with higher positions and customized schedules to members with different job responsibilities. They can also adjust the amount and format of information provided according to each member's position and job responsibilities. This allows the service provider to provide the most suitable schedule for each member based on their position and job responsibilities.

[0065] The scheduling department can adjust schedules while considering the project progress of each member. For example, it can propose an optimal schedule based on project progress. It can analyze project progress and improve scheduling efficiency. It can set schedule priorities by referring to project progress. As a result, the scheduling department can make more accurate adjustments by considering project progress.

[0066] The receiving unit can adjust its response method when receiving user complaints and grievances, taking into account the communication style of each member. For example, it can apply appropriate response methods to members with different communication styles. It can analyze communication styles and propose the optimal response method. It can set priority for responses according to communication styles. As a result, the receiving unit can provide the optimal response according to the communication style of each member.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects schedule data. For example, it can obtain schedule data from each member's calendar. The collection unit can automatically collect schedule data by linking with each member's calendar application. Alternatively, it can obtain schedule data from each member's calendar application via an API. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the collected schedule data can be statistically analyzed. Furthermore, the collected schedule data can be analyzed using pattern recognition technology or machine learning algorithms. Step 3: The optimization unit optimizes the schedule based on the analysis results obtained by the analysis unit. For example, the schedule can be made more efficient based on the analysis results. Furthermore, the optimal allocation of resources and schedule priorities can be set. Step 4: The service provider provides the optimized schedule created by the optimization service provider. For example, it can notify the user of the optimized schedule. Furthermore, it can automatically add the optimized schedule to the user's calendar or send it via email.

[0069] (Example of form 2) An AI agent schedule optimizer according to an embodiment of the present invention is a system that optimizes the schedules of members within an organization or team. In this system, users request schedule optimization via chat or voice input, and the AI ​​agent collects and analyzes each member's schedule data to provide an optimal schedule. This streamlines the meeting scheduling process and frees up members' working time. Furthermore, the AI ​​agent addresses complaints and dissatisfaction, ensuring fair scheduling. For example, the AI ​​agent schedule optimizer receives a request for schedule optimization via chat or voice input. For instance, if a user requests, "Please adjust next week's meeting," the AI ​​agent collects each member's schedule data. Next, the AI ​​agent analyzes the collected data to find common free time slots. For example, the AI ​​agent analyzes each member's calendar to identify time slots when everyone is free. Then, the AI ​​agent optimizes the schedule based on the analysis results. For example, the AI ​​agent proposes the optimal meeting time and notifies each member. Finally, the AI ​​agent provides the optimized schedule. For example, the AI ​​agent automatically adds the optimized schedule to each member's calendar. This streamlines the meeting scheduling process, freeing up members' work time. Furthermore, the AI ​​agent has the ability to address complaints and grievances. For example, if a member is dissatisfied with their schedule, the AI ​​agent will acknowledge their concerns and take appropriate action. This ensures fair scheduling. As a result, the AI ​​agent schedule optimizer can efficiently optimize the schedules of members within an organization or team, achieving fair scheduling.

[0070] The AI ​​agent schedule optimizer according to this embodiment comprises a collection unit, an analysis unit, an optimization unit, and a provision unit. The collection unit collects schedule data. The collection unit can, for example, obtain schedule data from each member's calendar. The collection unit can, for example, automatically collect schedule data in cooperation with each member's calendar application. The collection unit can, for example, obtain schedule data from each member's calendar application via an API. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, perform statistical analysis on the collected schedule data. The analysis unit can, for example, analyze the collected schedule data using pattern recognition technology. The analysis unit can, for example, analyze the collected schedule data using machine learning algorithms. The optimization unit optimizes the schedule based on the analysis results obtained by the analysis unit. The optimization unit can, for example, improve the efficiency of the schedule based on the analysis results. The optimization unit can, for example, optimize resource allocation based on the analysis results. The optimization unit can, for example, set schedule priorities based on the analysis results. The provision unit provides the schedule optimized by the optimization unit. The service provider can, for example, notify the user of the optimized schedule. The service provider can, for example, automatically add the optimized schedule to the user's calendar. The service provider can, for example, send the optimized schedule to the user via email. This allows the AI ​​agent schedule optimizer according to the embodiment to efficiently collect, analyze, optimize, and provide schedule data.

[0071] The data collection unit collects schedule data. Specifically, it can automatically collect schedule data by linking with each member's calendar application. For example, the data collection unit obtains schedule data from each member's calendar application via an API. In this process, the data collection unit provides appropriate authentication information to each member's calendar application and obtains the necessary access permissions. This allows the data collection unit to obtain each member's schedule data in real time and store it in a central database. Furthermore, the data collection unit can flexibly set the frequency and timing of schedule data collection. For example, it can be set to collect schedule data at a fixed time every day, or to collect data immediately when there is a change in the schedule. This ensures that the data collection unit always maintains the latest schedule data, making it available to the analysis and optimization units. In addition, the data collection unit has a checking function to ensure data integrity and consistency when collecting schedule data. For example, it can detect duplicate schedules and inconsistent data and process them appropriately. This allows the data collection unit to provide accurate and reliable schedule data.

[0072] The analysis unit analyzes the data collected by the data collection unit. Specifically, it performs statistical analysis on the collected schedule data to understand each member's schedule patterns and trends. For example, the analysis unit can identify busy and free time slots based on each member's schedule data, and use this information to improve schedule efficiency. The analysis unit can also use pattern recognition technology to find specific patterns and regularities within the schedule data. For example, it can detect patterns of regularly repeated meetings and events and optimize schedules based on these patterns. Furthermore, the analysis unit can use machine learning algorithms to analyze schedule data. For example, it can learn from past schedule data to help predict and optimize future schedules. Machine learning algorithms can extract important features from the schedule data and use them to improve schedule efficiency and optimize resource allocation. In this way, the analysis unit can analyze the collected schedule data from multiple perspectives and provide the information necessary for schedule optimization.

[0073] The optimization unit optimizes the schedule based on the analysis results obtained by the analysis unit. Specifically, it can improve the efficiency of the schedule based on the analysis results. For example, it optimizes meeting and event schedules by considering each member's busy and free times. The optimization unit can also optimize resource allocation. For example, it can optimize project resources based on each member's schedule to achieve efficient work. Furthermore, the optimization unit can set schedule priorities. For example, it can prioritize scheduling high-priority tasks and meetings to achieve efficient schedule management. The optimization unit can perform these optimization processes automatically and provide the user with an optimized schedule. In this way, the optimization unit can improve the efficiency of the schedule and optimize resource allocation, thereby improving the user's work efficiency.

[0074] The service provider provides the optimized schedule, which has been optimized by the optimization unit. Specifically, it can notify users of the optimized schedule. For example, the service provider can automatically add the optimized schedule to the user's calendar. This allows the user to check the optimized schedule and make changes or adjustments to it. The service provider can also send the optimized schedule to the user via email. This allows the user to make changes or adjustments to the schedule via email. Furthermore, the service provider can notify the user of the optimized schedule on their smartphone. This allows the user to make changes or adjustments to the schedule via their smartphone. Through these notification functions, the service provider can quickly provide users with optimized schedules and support the efficiency of their schedules. In this way, the service provider can streamline the user's schedule management and improve work efficiency.

[0075] The receiving unit can receive complaints and grievances. For example, the receiving unit can receive complaints and grievances from users via chat. For example, the receiving unit can receive complaints and grievances from users via voice input. For example, the receiving unit can receive complaints and grievances from users via email. In this way, the receiving unit can improve user satisfaction by receiving complaints and grievances. Some or all of the above processing in the receiving unit may be performed using, for example, a generating AI, or without a generating AI. For example, the receiving unit can input complaints and grievances from users into a generating AI, and the generating AI can generate an appropriate response.

[0076] The adjustment unit can adjust the schedule. For example, the adjustment unit can receive schedule change requests from users. For example, the adjustment unit can readjust the schedule based on schedule change requests from users. For example, the adjustment unit can propose an optimal schedule again based on schedule change requests from users. This allows the adjustment unit to adjust the schedule efficiently. Some or all of the above processes in the adjustment unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the adjustment unit can input a schedule change request from a user into a generation AI, and the generation AI can propose an optimal schedule again.

[0077] The reception desk can receive instructions from users. For example, the reception desk can receive schedule optimization requests from users via chat. For example, the reception desk can receive schedule optimization requests from users via voice input. For example, the reception desk can receive schedule optimization requests from users via email. This allows the reception desk to efficiently receive instructions from users. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input a schedule optimization request from a user into a generative AI, and the generative AI can generate an appropriate response.

[0078] The data collection unit can collect schedule data from each member. The data collection unit can, for example, obtain schedule data from each member's calendar. The data collection unit can, for example, automatically collect schedule data by coordinating with each member's calendar application. The data collection unit can, for example, obtain schedule data from each member's calendar application via an API. This allows the data collection unit to efficiently collect schedule data from each member. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the schedule data obtained from each member's calendar application into a generative AI, which can then analyze the data.

[0079] The analysis unit can analyze the collected data and find common free time. For example, the analysis unit can statistically analyze the collected schedule data to identify common free time. For example, the analysis unit can analyze the collected schedule data using pattern recognition technology to find common free time. For example, the analysis unit can analyze the collected schedule data using machine learning algorithms to find common free time. This allows the analysis unit to efficiently find common free time. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected schedule data into a generative AI, which can then identify common free time.

[0080] The optimization unit can optimize the schedule based on the analysis results. For example, the optimization unit can improve the efficiency of the schedule based on the analysis results. For example, the optimization unit can optimize the allocation of resources based on the analysis results. For example, the optimization unit can set schedule priorities based on the analysis results. This allows the optimization unit to efficiently optimize the schedule. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the optimization unit can input the analysis results into a generation AI, which can then generate an optimal schedule.

[0081] The service provider can provide an optimized schedule. The service provider can, for example, notify the user of the optimized schedule. The service provider can, for example, automatically add the optimized schedule to the user's calendar. The service provider can, for example, send the optimized schedule to the user via email. This allows the service provider to efficiently provide the optimized schedule. Some or all of the above processes in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the optimized schedule into a generative AI, and the generative AI can generate an appropriate notification method.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of schedule data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, if the user is relaxed, the data collection unit can accelerate the collection timing to efficiently collect data. For example, if the user is in a hurry, the data collection unit can collect data immediately and quickly optimize the schedule. In this way, the data collection unit can reduce the user's burden by adjusting the collection timing 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 a generative AI, for example, or without a generative AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can then adjust the collection timing.

[0083] The data collection unit can analyze each member's past schedule history and select the optimal data collection method. For example, the data collection unit can analyze each member's past schedule history and select the most efficient data collection method. For example, the data collection unit can optimize the data collection frequency based on each member's past schedule history. For example, the data collection unit can adjust the data collection timing based on each member's past schedule history. In this way, the data collection unit can select the optimal data collection method by analyzing past schedule history. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input each member's past schedule history into a generating AI, and the generating AI can select the optimal data collection method.

[0084] The data collection unit can filter schedule data based on each member's current projects and areas of interest when collecting it. For example, the data collection unit can prioritize collecting relevant schedule data based on each member's current projects. For example, the data collection unit can filter relevant schedule data based on each member's areas of interest. For example, the data collection unit can collect optimal schedule data by considering each member's current projects and areas of interest. This allows the data collection unit to collect highly relevant data by filtering the data based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input data on each member's current projects and areas of interest into a generative AI, which can then perform optimal filtering.

[0085] The data collection unit can estimate the user's emotions and determine the priority of schedule data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important schedule data. For example, if the user is relaxed, the data collection unit can prioritize the collection of important schedule data. For example, if the user is in a hurry, the data collection unit can prioritize the most important schedule data. In this way, the data collection unit can efficiently collect data by determining the priority of data 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 a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can determine the priority of schedule data to collect.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each member when collecting schedule data. For example, the data collection unit can prioritize the collection of highly relevant schedule data based on the geographical location information of each member. For example, the data collection unit can determine the optimal collection timing by considering the geographical location information of each member. For example, the data collection unit can determine the priority of schedule data to be collected based on the geographical location information of each member. In this way, the data collection unit can efficiently collect highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input the geographical location information of each member into a generating AI, and the generating AI can select the optimal collection method.

[0087] The data collection unit can analyze each member's social media activity and collect relevant data when collecting schedule data. For example, the data collection unit can analyze each member's social media activity and collect relevant schedule data. For example, the data collection unit can determine the priority of schedule data to collect based on each member's social media activity. For example, the data collection unit can select the optimal data collection method considering each member's social media activity. This allows the data collection unit to efficiently collect relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input each member's social media activity data into a generative AI, which can then select the optimal data collection method.

[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 stressed, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis 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 a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative 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 schedule data during the analysis. For example, the analysis unit can perform a detailed analysis on schedule data with high importance. For example, the analysis unit can perform a simplified analysis on schedule data with low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the schedule data. This allows the analysis unit to perform analysis efficiently by adjusting the level of detail of the analysis according to the importance of the schedule data. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the importance of the schedule data into the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of the schedule data during analysis. For example, the analysis unit can apply the optimal analysis algorithm to meeting schedules. For example, the analysis unit can apply different analysis algorithms to project schedules. For example, the analysis unit can select the optimal analysis algorithm depending on the category of the schedule data. This allows the analysis unit to improve the accuracy of the analysis by applying the optimal analysis algorithm according to the category of the schedule data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the category of the schedule data into the generative AI, and the generative AI can select the optimal 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 stressed, 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 in a hurry, the analysis unit can provide a quick analysis result. In this way, the analysis unit can provide an appropriate analysis result for the user by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI, and the generative AI can adjust the length of the analysis.

[0092] The analysis unit can determine the priority of analysis based on the submission timing of the schedule data during the analysis. For example, the analysis unit may prioritize the analysis of schedule data with an approaching submission deadline. For example, the analysis unit may postpone the analysis of schedule data with a distant submission deadline. The analysis unit can dynamically adjust the priority of analysis based on the submission timing. This allows the analysis unit to perform analysis efficiently by determining the priority of analysis based on the submission timing. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the submission timing of the schedule data into the generating AI, and the generating AI can determine the priority of analysis.

[0093] The analysis unit can adjust the order of analysis based on the relationships between schedule data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant schedule data. For example, the analysis unit may postpone the analysis of less relevant schedule data. The analysis unit can dynamically adjust the order of analysis based on the relationships between schedule data. This allows the analysis unit to perform analysis efficiently by adjusting the order of analysis based on relationships. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the relationships between schedule data into a generating AI, and the generating AI can adjust the order of analysis.

[0094] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated emotions. For example, if the user is stressed, the optimization unit can apply optimization criteria that reduce the burden. For example, if the user is relaxed, the optimization unit can apply optimization criteria that prioritize efficiency. For example, if the user is in a hurry, the optimization unit can apply criteria that optimize quickly. In this way, the optimization unit can provide the user with the optimal schedule by adjusting the optimization criteria 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 optimization unit may be performed using a generative AI, or not using a generative AI. For example, the optimization unit can input user emotion data into a generative AI, and the generative AI can adjust the optimization criteria.

[0095] The optimization unit can improve the accuracy of optimization by considering the interrelationships of schedule data during optimization. For example, the optimization unit analyzes the interrelationships of schedule data and proposes an optimal schedule. The optimization unit can improve the accuracy of optimization by considering the interrelationships of schedule data. For example, the optimization unit can generate an optimal schedule based on the interrelationships of schedule data. In this way, the optimization unit can improve the accuracy of optimization by considering the interrelationships of schedule data. Some or all of the above processing in the optimization unit may be performed using a generation AI, for example, or without a generation AI. For example, the optimization unit can input the interrelationships of schedule data to a generation AI, and the generation AI can generate an optimal schedule.

[0096] The optimization unit can perform optimization while considering the attribute information of each member. For example, the optimization unit can propose an optimal schedule by considering each member's position and job duties. For example, the optimization unit can improve the accuracy of optimization based on each member's attribute information. For example, the optimization unit can generate an optimal schedule by considering each member's attribute information. In this way, the optimization unit can generate an optimal schedule by considering each member's attribute information. Some or all of the above processing in the optimization unit may be performed using a generation AI, for example, or without a generation AI. For example, the optimization unit can input each member's attribute information into a generation AI, and the generation AI can generate an optimal schedule.

[0097] The optimization unit can estimate the user's emotions and adjust the order in which the optimization results are displayed based on the estimated emotions. For example, if the user is stressed, the optimization unit will display the most important results first. If the user is relaxed, the optimization unit can display detailed results in a sequential manner. If the user is in a hurry, the optimization unit can quickly display the most important results. In this way, the optimization unit can provide results that are easy for the user to understand by adjusting the order in which the results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 optimization unit may be performed using a generative AI, or not using a generative AI. For example, the optimization unit can input user emotion data into a generative AI, and the generative AI can adjust the order in which the results are displayed.

[0098] The optimization unit can perform optimization while considering the geographical distribution of schedule data. For example, the optimization unit proposes an optimal schedule based on the geographical distribution of schedule data. For example, the optimization unit can improve the accuracy of optimization by considering the geographical distribution of schedule data. For example, the optimization unit can generate an optimal schedule based on the geographical distribution of schedule data. In this way, the optimization unit can generate an optimal schedule by considering the geographical distribution of schedule data. Some or all of the above processing in the optimization unit may be performed using a generation AI, for example, or without a generation AI. For example, the optimization unit can input the geographical distribution of schedule data to a generation AI, and the generation AI can generate an optimal schedule.

[0099] The optimization unit can improve the accuracy of optimization by referring to relevant literature on the schedule data during optimization. For example, the optimization unit can refer to relevant literature on the schedule data and propose an optimal schedule. For example, the optimization unit can improve the accuracy of optimization based on relevant literature on the schedule data. For example, the optimization unit can generate an optimal schedule by referring to relevant literature on the schedule data. In this way, the optimization unit can improve the accuracy of optimization by referring to relevant literature. Some or all of the above processing in the optimization unit may be performed using a generation AI, for example, or without a generation AI. For example, the optimization unit can input relevant literature on the schedule data into a generation AI, and the generation AI can generate an optimal schedule.

[0100] The service provider can estimate the user's emotions and adjust the display method of the schedule based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, by adjusting the display method according to the user's emotions, the service provider can provide a schedule that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using a generative AI, for example, or without a generative AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can adjust the display method.

[0101] The information provider can adjust the level of detail provided based on the importance of the schedule at the time of provision. For example, the provider can provide detailed information for schedules of high importance. For example, the provider can provide simplified information for schedules of low importance. The provider can dynamically adjust the level of detail provided according to the importance of the schedule. This allows the provider to efficiently provide information by adjusting the level of detail provided according to the importance of the schedule. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without a generating AI. For example, the provider can input the importance of the schedule into the generating AI, and the generating AI can adjust the level of detail provided.

[0102] The information delivery unit can apply different delivery algorithms depending on the schedule category at the time of delivery. For example, the information delivery unit can apply the optimal delivery algorithm to a meeting schedule. For example, the information delivery unit can apply a different delivery algorithm to a project schedule. For example, the information delivery unit can select the optimal delivery algorithm depending on the schedule category. This allows the information delivery unit to improve the accuracy of information delivery by applying the optimal delivery algorithm according to the schedule category. Some or all of the above processing in the information delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information delivery unit can input the schedule category into a generative AI, and the generative AI can select the optimal delivery algorithm.

[0103] The service provider can estimate the user's emotions and adjust the length of the schedule provided based on the estimated emotions. For example, if the user is stressed, the service provider can provide a short, concise schedule. For example, if the user is relaxed, the service provider can provide a detailed schedule. For example, if the user is in a hurry, the service provider can adjust the schedule to be delivered quickly. In this way, the service provider can provide an appropriate schedule for the user by adjusting the length of the schedule according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can adjust the length of the schedule.

[0104] The service provider can determine the priority of service provision based on the submission timing of the schedules. For example, the service provider can prioritize providing schedules with approaching deadlines. For example, the service provider can postpone providing schedules with distant deadlines. The service provider can dynamically adjust the priority of service provision based on the submission timing. This allows the service provider to efficiently provide information by determining the priority of service provision based on the submission timing. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input the submission timing of the schedules into a generating AI, and the generating AI can determine the priority of service provision.

[0105] The service provider can adjust the order of delivery based on the relevance of the schedules at the time of delivery. For example, the service provider can prioritize the delivery of highly relevant schedules. For example, the service provider can postpone less relevant schedules. The service provider can dynamically adjust the order of delivery based on the relevance of the schedules. This allows the service provider to efficiently deliver information by adjusting the order of delivery based on relevance. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the relevance of the schedules into a generative AI, and the generative AI can adjust the order of delivery.

[0106] The receiving unit can estimate the user's emotions and adjust how it receives complaints based on the estimated emotions. For example, if the user is stressed, the receiving unit can apply a gentle receiving method. For example, if the user is relaxed, the receiving unit can apply a receiving method that includes detailed explanations. For example, if the user is in a hurry, the receiving unit can apply a receiving method that responds quickly. In this way, the receiving unit can appropriately receive the user's complaints by adjusting the receiving method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 receiving unit may be performed using a generative AI, or not using a generative AI. For example, the receiving unit can input user emotion data into a generative AI, and the generative AI can adjust the receiving method.

[0107] The receiving unit can select the optimal receiving method by referring to past complaint data when receiving feedback. For example, the receiving unit can refer to past complaint data and select the optimal receiving method. For example, the receiving unit can adjust the receiving method based on past complaint data. For example, the receiving unit can analyze past complaint data and propose the optimal receiving method. In this way, the receiving unit can select the optimal receiving method by referring to past complaint data. Some or all of the above processing in the receiving unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the receiving unit can input past complaint data into a generating AI, and the generating AI can select the optimal receiving method.

[0108] The receiving unit can estimate the user's emotions and prioritize complaints based on the estimated emotions. For example, if the user is stressed, the receiving unit will prioritize receiving high-priority complaints. For example, if the user is relaxed, the receiving unit can prioritize receiving detailed complaints. For example, if the user is in a hurry, the receiving unit can prioritize receiving complaints that require a quick response. In this way, the receiving unit can prioritize receiving important complaints by prioritizing complaints 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 receiving unit may be performed using a generative AI, or not using a generative AI. For example, the receiving unit can input user emotion data into a generative AI, which can then determine the priority of complaints.

[0109] The receiving unit can select the optimal receiving method when receiving data, taking into account the user's geographical location information. For example, the receiving unit can select the optimal receiving method based on the user's geographical location information. For example, the receiving unit can adjust the receiving method, taking into account the user's geographical location information. For example, the receiving unit can determine the priority of receiving data based on the user's geographical location information. In this way, the receiving unit can select the optimal receiving method by taking into account geographical location information. Some or all of the above processing in the receiving unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the receiving unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal receiving method.

[0110] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated user emotions. For example, if the user is stressed, the adjustment unit can apply an adjustment method that reduces the burden. For example, if the user is relaxed, the adjustment unit can apply an adjustment method that prioritizes efficiency. For example, if the user is in a hurry, the adjustment unit can apply a method that makes adjustments quickly. In this way, the adjustment unit can make the optimal adjustment for the user by adjusting the adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the adjustment unit may be performed using a generative AI, for example, or without a generative AI. For example, the adjustment unit can input the user's emotion data into a generative AI, and the generative AI can adjust the adjustment method.

[0111] The adjustment unit can select the optimal adjustment method by referring to past adjustment data during adjustment. For example, the adjustment unit can refer to past adjustment data and select the optimal adjustment method. For example, the adjustment unit can adjust the adjustment method based on past adjustment data. For example, the adjustment unit can analyze past adjustment data and propose the optimal adjustment method. In this way, the adjustment unit can select the optimal adjustment method by referring to past adjustment data. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit can input past adjustment data into a generating AI, and the generating AI can select the optimal adjustment method.

[0112] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the user is stressed, the adjustment unit will prioritize high-priority adjustments. For example, if the user is relaxed, the adjustment unit can perform detailed adjustments. For example, if the user is in a hurry, the adjustment unit can prioritize adjustments that require a quick response. In this way, the adjustment unit can prioritize important adjustments by determining the priority of adjustments 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 adjustment unit may be performed using a generative AI, or not using a generative AI. For example, the adjustment unit can input user emotion data into a generative AI, and the generative AI can determine the priority of adjustments.

[0113] The adjustment unit can select the optimal adjustment method during adjustment, taking into account the user's geographical location information. For example, the adjustment unit selects the optimal adjustment method based on the user's geographical location information. For example, the adjustment unit can adjust the adjustment method, taking into account the user's geographical location information. For example, the adjustment unit can determine the priority of adjustments based on the user's geographical location information. In this way, the adjustment unit can select the optimal adjustment method by taking geographical location information into account. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal adjustment method.

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

[0115] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, high-priority analyses can be prioritized. If the user is relaxed, detailed analyses can be performed. If the user is in a hurry, analyses can be performed quickly. In this way, the analysis unit can perform analyses efficiently by determining the priority of analyses according to the user's emotions.

[0116] The service provider can estimate the user's emotions and adjust the format of the schedule provided based on those emotions. For example, if the user is stressed, the schedule can be presented in a simple and highly visible format. If the user is relaxed, it can be presented in a format that includes detailed information. If the user is in a hurry, it can be presented in a format that gets straight to the point. In this way, the service provider can provide a schedule that is easy for the user to understand by adjusting the format of the schedule according to the user's emotions.

[0117] The adjustment unit can estimate the user's emotions and determine the frequency of adjustments based on those emotions. For example, if the user is stressed, the frequency of adjustments can be reduced to alleviate their burden. If the user is relaxed, the frequency of adjustments can be increased to improve efficiency. If the user is in a hurry, adjustments can be made quickly. In this way, the adjustment unit can determine the frequency of adjustments according to the user's emotions, thereby providing the optimal adjustment for the user.

[0118] The receiving unit can estimate the user's emotions and adjust its response to complaints and grievances based on those emotions. For example, if the user is stressed, it can respond quickly and courteously. If the user is relaxed, it can provide a response that includes detailed explanations. If the user is in a hurry, it can respond concisely. In this way, the receiving unit can improve user satisfaction by adjusting its response to complaints and grievances according to the user's emotions.

[0119] The data collection unit can estimate the user's emotions and adjust the range of data collected based on those emotions. For example, if the user is stressed, only the minimum necessary data can be collected. If the user is relaxed, detailed data can be collected. If the user is in a hurry, data can be collected quickly. In this way, the data collection unit can efficiently collect data by adjusting the range of data collected according to the user's emotions.

[0120] The analysis unit can perform analysis of schedule data while considering each member's past performance data. For example, it can propose an optimal schedule for each member based on past performance data. It can analyze past performance data to improve schedule efficiency. It can set schedule priorities by referring to past performance data. As a result, the analysis unit can perform more accurate analysis by considering past performance data.

[0121] The optimization unit can optimize schedule data while considering each member's health data. For example, it can propose a schedule that reduces the burden on each member based on their health data. It can analyze health data and generate an optimal schedule according to their health status. It can also set schedule priorities by referring to health data. In this way, the optimization unit can provide an optimal schedule that takes each member's health status into consideration by taking health data into account.

[0122] The service provider can adjust the method of providing schedules according to each member's position and job responsibilities. For example, they can provide detailed schedules to members with higher positions and customized schedules to members with different job responsibilities. They can also adjust the amount and format of information provided according to each member's position and job responsibilities. This allows the service provider to provide the most suitable schedule for each member based on their position and job responsibilities.

[0123] The scheduling department can adjust schedules while considering the project progress of each member. For example, it can propose an optimal schedule based on project progress. It can analyze project progress and improve scheduling efficiency. It can set schedule priorities by referring to project progress. As a result, the scheduling department can make more accurate adjustments by considering project progress.

[0124] The receiving unit can adjust its response method when receiving user complaints and grievances, taking into account the communication style of each member. For example, it can apply appropriate response methods to members with different communication styles. It can analyze communication styles and propose the optimal response method. It can set priority for responses according to communication styles. As a result, the receiving unit can provide the optimal response according to the communication style of each member.

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

[0126] Step 1: The collection unit collects schedule data. For example, it can obtain schedule data from each member's calendar. The collection unit can automatically collect schedule data by linking with each member's calendar application. Alternatively, it can obtain schedule data from each member's calendar application via an API. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the collected schedule data can be statistically analyzed. Furthermore, the collected schedule data can be analyzed using pattern recognition technology or machine learning algorithms. Step 3: The optimization unit optimizes the schedule based on the analysis results obtained by the analysis unit. For example, the schedule can be made more efficient based on the analysis results. Furthermore, the optimal allocation of resources and schedule priorities can be set. Step 4: The service provider provides the optimized schedule created by the optimization service provider. For example, it can notify the user of the optimized schedule. Furthermore, it can automatically add the optimized schedule to the user's calendar or send it via email.

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

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

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

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, optimization unit, provision unit, receiving unit, adjustment unit, and reception unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect schedule data in cooperation with the calendar application of the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes the schedule by the specific processing unit 290 of the data processing unit 12. The provision unit provides the optimized schedule by the control unit 46A of the smart device 14. The receiving unit receives complaints using the chat function of the smart device 14. The adjustment unit adjusts the schedule by the control unit 46A of the smart device 14. The reception unit receives instructions from the user using the voice input function of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, optimization unit, provision unit, receiving unit, adjustment unit, and reception unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect schedule data in cooperation with the calendar application of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes the schedule by the specific processing unit 290 of the data processing unit 12. The provision unit provides the optimized schedule by the control unit 46A of the smart glasses 214. The receiving unit receives complaints using the chat function of the smart glasses 214. The adjustment unit adjusts the schedule by the control unit 46A of the smart glasses 214. The reception unit receives instructions from the user using the voice input function of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, optimization unit, provision unit, receiving unit, adjustment unit, and reception unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect schedule data in cooperation with the calendar application of the headset terminal 314. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes the schedule by the specific processing unit 290 of the data processing unit 12. The provision unit provides the optimized schedule by the control unit 46A of the headset terminal 314. The receiving unit receives complaints using the chat function of the headset terminal 314. The adjustment unit adjusts the schedule using the control unit 46A of the headset terminal 314. The reception unit receives instructions from the user using the voice input function of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0179] Each of the multiple elements described above, including the collection unit, analysis unit, optimization unit, provision unit, receiving unit, adjustment unit, and reception unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect schedule data in cooperation with the robot 414's calendar application. The analysis unit analyzes the collected data by, for example, the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes the schedule by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the optimized schedule by, for example, the control unit 46A of the robot 414. The receiving unit receives complaints and grievances using, for example, the robot 414's chat function. The adjustment unit adjusts the schedule by, for example, the control unit 46A of the robot 414. The reception unit receives instructions from the user using, for example, the robot 414's voice input function. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

[0189] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0198] (Note 1) A collection unit that collects schedule data, An analysis unit analyzes the data collected by the aforementioned collection unit, An optimization unit optimizes the schedule based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides a schedule optimized by the optimization unit. A system characterized by the following features. (Note 2) Equipped with a section to receive complaints and grievances. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a scheduling unit for adjusting the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a reception desk to receive instructions from users. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect schedule data for each member. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze the collected data to find common free time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The optimization unit, Optimize the schedule based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, Provides an optimized schedule The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of schedule data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze each member's past schedule history and select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting schedule data, filter it based on each member's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of scheduled data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting schedule data, the system prioritizes collecting highly relevant data by considering each member's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting schedule data, analyze each member's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the schedule data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the scheduled data. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the submission timing of the scheduled data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the schedule data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, It estimates user sentiment and adjusts optimization criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, During optimization, consider the interrelationships of schedule data to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, During optimization, the attribute information of each member is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, It estimates the user's emotions and adjusts the order in which the optimization results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, During optimization, the geographical distribution of the schedule data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, During optimization, we improve the accuracy of the optimization by referring to relevant literature on the schedule data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the schedule is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, adjust the level of detail based on the importance of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, different delivery algorithms are applied depending on the schedule category. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the schedule provided based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, we will determine the priority of the service based on the submission date of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the service, adjust the order of delivery based on the relevance of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 33) The receiving portion is, It estimates the user's emotions and adjusts how complaints and dissatisfaction are handled based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The receiving portion is, When receiving feedback, the optimal method of receiving feedback is selected by referring to past data on complaints and dissatisfaction. The system described in Appendix 2, characterized by the features described herein. (Note 35) The receiving portion is, It estimates the user's emotions and prioritizes complaints based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The receiving portion is, When receiving data, the system selects the optimal receiving method, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The adjustment unit is, It estimates the user's emotions and adjusts the adjustment method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The adjustment unit is, During adjustment, past adjustment data is referenced to select the optimal adjustment method. The system described in Appendix 3, characterized by the features described herein. (Note 39) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The adjustment unit is, During the adjustment process, the optimal adjustment method is selected by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A collection unit that collects schedule data, An analysis unit analyzes the data collected by the aforementioned collection unit, An optimization unit optimizes the schedule based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides a schedule optimized by the optimization unit. A system characterized by the following features.

2. Equipped with a section to receive complaints and grievances. The system according to feature 1.

3. It includes a scheduling unit for adjusting the schedule. The system according to feature 1.

4. It is equipped with a reception desk to receive instructions from users. The system according to feature 1.

5. The aforementioned collection unit is Collect schedule data for each member. The system according to feature 1.

6. The aforementioned analysis unit, Analyze the collected data to find common free time. The system according to feature 1.

7. The optimization unit, Optimize the schedule based on the analysis results. The system according to feature 1.

8. The aforementioned supply unit is, Provides an optimized schedule The system according to feature 1.

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