system

Abstract

The system according to this embodiment aims to optimally coordinate the work-life balance of business professionals. [Solution] The system according to the embodiment comprises a learning unit, an adjustment unit, a prediction unit, and a private management unit. The learning unit learns behavioral patterns and work patterns. The adjustment unit adjusts meetings and manages tasks based on the data learned by the learning unit. The prediction unit predicts and shares unexpected troubles based on the meetings and tasks adjusted by the adjustment unit. The private management unit manages private schedules based on the troubles predicted by the prediction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to effectively solve the problems of long working hours and work-life balance of business people.

[0005] The system according to the embodiment aims to optimally coordinate the balance between the work and private life of business people.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, an adjustment unit, a prediction unit, and a private management unit. The learning unit learns behavioral patterns and work patterns. The adjustment unit performs meeting scheduling and task management based on the data learned by the learning unit. The prediction unit predicts and shares unexpected problems based on the meetings and tasks adjusted by the adjustment unit. The private management unit manages private schedules based on the problems predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimally coordinate the work-life balance of business professionals. [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 2 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The agent AI system according to an embodiment of the present invention is a system designed to solve problems faced by Japanese business people, such as long working hours and overwork, work-life balance, career stagnation, and future anxieties. This agent AI system takes all of each business person's behavioral patterns, created data, and information transmission (email, messaging apps, phone calls, etc.) as input and the AI ​​learns from it. Next, based on the data the AI ​​has learned, it coordinates all of the troublesome tasks of meeting scheduling, preparation, follow-up, and meeting member selection. It also shares predicted problems and events with business people in advance, before unexpected troubles occur. The final decision is made by each business person, but the AI's support allows them to create more time for themselves and relieve stress. Furthermore, the AI ​​learns the behavioral and work patterns of business people and provides an optimal environment. For example, it checks work tasks, cross-checks for missed tasks and human errors, detects problems in advance, and adjusts schedules. In this way, it improves the stress levels of business people and helps them regain their happiness. This AI agent system not only manages office work but also optimally coordinates personal concerns. For example, it manages personal schedules such as weekend events, family time, and local social activities, supporting business professionals so they can do what they truly want to do, experience what they want to experience, and focus on what they want to do. In this way, the AI ​​agent system can solve business professionals' problems and enable them to perform their work efficiently while also enriching their personal lives.

[0029] The agent AI system according to this embodiment comprises a learning unit, a coordination unit, a prediction unit, and a private management unit. The learning unit learns behavioral patterns and work patterns. For example, the learning unit learns by inputting the behavioral patterns, created data, and information transmission (email, messaging apps, phone calls, etc.) of each business person. The coordination unit performs meeting coordination and task management based on the data learned by the learning unit. For example, the coordination unit handles tedious meeting coordination, preparation, follow-up, and meeting member coordination. The prediction unit predicts and shares unexpected troubles based on the meetings and tasks coordinated by the coordination unit. For example, the prediction unit predicts troubles such as system failures, human errors, and troubles caused by external factors, and shares them with business people. The private management unit manages private schedules based on troubles predicted by the prediction unit. For example, the private management unit manages private schedules such as weekend events, family activities, and local social activities. This allows the agent AI system to learn the behavioral and work patterns of business professionals, enabling efficient work execution and a fulfilling personal life by handling meeting scheduling, task management, trouble prediction, and personal schedule management.

[0030] The learning unit learns behavioral and work patterns. For example, it learns by inputting each business person's behavioral patterns, created data, and information transmission (email, messaging apps, phone calls, etc.). Specifically, it collects the history of daily tasks and communications performed by business people and analyzes behavioral patterns based on this data. For example, it collects email sending and receiving history, messaging app chat logs, and phone call records, and analyzes this data using natural language processing technology. Furthermore, it collects creation data such as documents, spreadsheets, and presentation materials created by business people, and extracts work patterns from this data. Using this data, the learning unit learns the workflow, priorities, and frequently performed tasks of business people, building a foundation for providing optimal support to individual business people. Moreover, the learning unit uses machine learning algorithms to continuously learn changes in business people's behavioral and work patterns, enabling it to provide support based on the latest information. For example, even if a new project starts or work content changes, the learning unit can respond quickly and provide support tailored to the needs of business people. This allows the learning department to play a crucial role in improving the work efficiency of business professionals.

[0031] The Coordination Unit performs meeting scheduling and task management based on data learned by the Learning Unit. For example, the Coordination Unit handles tedious tasks such as meeting scheduling, preparation, follow-up, and coordinating meeting members. Specifically, it proposes optimal meeting schedules based on the behavioral and work patterns of business professionals collected by the Learning Unit. For instance, it automatically detects each member's availability and finds a time slot where everyone can participate. It also prepares necessary materials and information in advance according to the meeting's purpose and agenda, supporting the smooth progress of the meeting. Furthermore, as a post-meeting follow-up, it automates the creation of meeting minutes and task assignment, notifying each member of appropriate tasks. Through these functions, the Coordination Unit significantly reduces the time business professionals spend on meeting scheduling and task management, allowing them to focus on more important tasks. The Coordination Unit also monitors task progress in real time and automatically reminds users of tasks with approaching deadlines or those that are incomplete. This prevents tasks from being missed or delayed, enabling business professionals to work efficiently. Furthermore, the coordination unit supports business professionals in focusing on their most important tasks by centrally managing and prioritizing multiple projects and tasks. This allows the coordination unit to play a crucial role in improving the work efficiency of business professionals.

[0032] The Forecasting Department predicts and shares potential problems based on meetings and tasks coordinated by the Coordination Department. For example, the Forecasting Department predicts system failures, human error, and problems caused by external factors, and shares this information with business personnel. Specifically, it uses historical data and statistical information, based on meeting schedules and task progress managed by the Coordination Department, to predict the probability of unexpected problems occurring. For example, it analyzes past meeting data to identify problems that are likely to occur under specific conditions. It also monitors task progress to predict the possibility of delays or incomplete tasks. Furthermore, it considers external factors such as weather information, traffic information, and economic trends, and predicts the impact these factors will have on business operations. The Forecasting Department shares these prediction results with business personnel in real time, providing information to enable them to take preventative measures. For example, if there is a high probability of a system failure, it notifies them to prepare a backup system. If there is a possibility of human error, it instructs them to be vigilant and perform additional checks. This allows the Forecasting Department to enable business personnel to respond quickly to unexpected problems, minimizing business interruptions and delays. Furthermore, the forecasting unit contributes to long-term risk management and the development of countermeasures based on the forecast results. For example, if a particular problem occurs frequently, identifying its cause and implementing fundamental countermeasures can reduce future risks. In this way, the forecasting unit can play an important role in improving the work efficiency of business people.

[0033] The Private Management Department manages personal schedules based on troubles predicted by the Forecasting Department. For example, it manages personal schedules such as weekend events, family activities, and local social engagements. Specifically, it centrally manages business professionals' personal schedules and identifies factors that may affect them based on trouble prediction information provided by the Forecasting Department. For instance, if a family event scheduled for the weekend may be affected by a predicted system failure or external factor, the Private Management Department will notify the business professional in advance and suggest alternative plans. Furthermore, if a business professional's personal schedule may impact work, such as an approaching deadline for an important meeting or task, the Private Management Department will suggest appropriate adjustments to the business professional. In addition, the Private Management Department has a function to optimize work schedules while considering business professionals' personal schedules. For example, it adjusts work schedules to ensure personal time is available to prioritize family time. The Private Management Department also contributes to the health and stress management of business professionals. For example, it suggests regular rest and refreshment time to support business professionals in maintaining a healthy lifestyle. This allows the private management department to play a crucial role in optimizing the work-life balance of business professionals and improving their overall quality of life.

[0034] The learning unit can learn by inputting the behavioral patterns, created data, and information transmission of each business person. For example, the learning unit can learn daily actions, actions during work, and actions under specific circumstances. The learning unit can also learn created data such as document data, spreadsheets, and presentation materials. Furthermore, the learning unit can learn communication methods such as email, chat, and telephone. This allows the learning unit to provide individually optimized support by learning the behavioral patterns, created data, and information transmission of each business person. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the behavioral patterns and created data of each business person into a generative AI, which can then perform the learning.

[0035] The coordination unit can perform tedious tasks such as meeting scheduling, preparation, follow-up, and meeting member selection based on data learned by the learning unit. For example, the coordination unit can schedule meetings, select participants, and reserve meeting rooms. It can also prepare materials, set up meeting rooms, and notify participants. Furthermore, it can perform follow-up tasks such as creating meeting minutes, reviewing action items, and scheduling follow-up meetings. This automates the tedious tasks of meeting scheduling, preparation, follow-up, and meeting member selection, thereby reducing the burden on business professionals. Some or all of the above-described processes in the coordination unit may be performed using, for example, a generative AI, or not. For instance, the coordination unit can input data learned by the learning unit into a generative AI, which can then perform meeting scheduling and task management.

[0036] The prediction unit can predict unexpected problems based on meetings and tasks coordinated by the coordination unit and share this information with business personnel. For example, the prediction unit can predict system failures, human errors, and problems caused by external factors. The prediction unit can also calculate the probability of a problem occurring and share this information with business personnel. Furthermore, the prediction unit can evaluate the scope of the problem's impact and share this information with business personnel. This allows for the prevention of problems by predicting unexpected issues and sharing them with business personnel in advance. Some or all of the above-described processes in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input data on meetings and tasks coordinated by the coordination unit into a generative AI, which can then perform the problem prediction.

[0037] The Private Management Department can manage private appointments such as weekend events, family activities, and local social activities based on troubles predicted by the Prediction Department. For example, the Private Management Department manages weekend events such as outings with family, hobby activities, and gatherings with friends. It can also manage family activities such as spending time with family, helping with household chores, and childcare. Furthermore, the Private Management Department can manage local social activities such as participating in community events and interacting with neighbors. In this way, by managing private appointments, it is possible to support business people in being able to do what they really want to do, experience what they want to experience, and focus on what they want to do. Some or all of the above processes in the Private Management Department may be performed using, for example, a Generative AI, or not using a Generative AI. For example, the Private Management Department can input trouble data predicted by the Prediction Department into a Generative AI, and the Generative AI can manage private appointments.

[0038] The adjustment unit can check work tasks, perform cross-checks for missed tasks and human errors, detect problems in advance, and adjust schedules. For example, the adjustment unit checks work tasks such as confirming task progress and completion status. The adjustment unit can also detect missed tasks, such as incomplete tasks and forgotten tasks. Furthermore, the adjustment unit can perform cross-checks for human errors, such as verification by multiple people and the use of error checklists. In this way, by checking work tasks, performing cross-checks for missed tasks and human errors, problems can be detected in advance and schedules can be adjusted. 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 work task data into a generation AI, which can then detect problems and adjust schedules.

[0039] The learning unit can analyze the user's past behavior patterns during learning and apply the optimal learning algorithm. For example, the learning unit can select the optimal learning algorithm based on tasks the user has frequently performed in the past. The learning unit can also suggest efficient learning methods based on the user's past behavior patterns. Furthermore, the learning unit can analyze the user's past behavior history and apply the most effective learning algorithm. This allows for the application of the optimal learning algorithm by analyzing the user's past behavior patterns, thereby improving learning efficiency. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input data on the user's past behavior patterns into a generative AI, which can then apply the optimal learning algorithm.

[0040] The learning unit can apply different learning methods depending on the user's job content during the learning process. For example, if the user is in sales, the learning unit will prioritize learning data related to sales. Similarly, if the user is in a technical position, the learning unit can prioritize learning data related to technology. Furthermore, if the user is in a management position, the learning unit can prioritize learning data related to management. This maximizes the effectiveness of learning by applying learning methods tailored to the user's job content. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input data on the user's job content into a generative AI, which can then apply different learning methods.

[0041] The learning unit can prioritize learning highly relevant data by considering the user's geographical location during the learning process. For example, if the user is in a specific region, the learning unit will prioritize learning data related to that region. Furthermore, if the user is on a business trip, the learning unit can prioritize learning data related to the destination. Additionally, if the user is at home, the learning unit can prioritize learning data related to their home. This allows for the prioritization of highly relevant data and enhances the effectiveness of the learning process by considering the user's geographical location. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's geographical location data into a generative AI, which can then prioritize learning highly relevant data.

[0042] The learning unit can analyze the user's social media activity and learn relevant data during the learning process. For example, the learning unit can learn data related to topics that the user frequently mentions on social media. It can also learn data related to topics that the user's social media followers are interested in. Furthermore, the learning unit can analyze the content of the user's social media posts and learn relevant data. This allows the learning unit to learn relevant data by analyzing the user's social media activity and improve the accuracy of the learning process. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input data on the user's social media activity into a generative AI, which can then learn the relevant data.

[0043] The scheduling unit can analyze the user's past meeting history and select the optimal scheduling method during scheduling. For example, the scheduling unit can propose the optimal meeting schedule based on the user's past meeting history. It can also propose efficient meeting scheduling methods based on the user's past meeting history. Furthermore, the scheduling unit can analyze the user's past meeting history and select the most effective scheduling method. This allows for the selection of the optimal scheduling method by analyzing the user's past meeting history, thereby improving meeting efficiency. Some or all of the above processing in the scheduling unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scheduling unit can input data on the user's past meeting history into a generative AI, which can then select the optimal scheduling method.

[0044] The scheduling unit can apply different scheduling methods depending on the user's job content during scheduling. For example, if the user is in sales, the scheduling unit will prioritize scheduling meetings related to sales. Similarly, if the user is in technical roles, the scheduling unit can prioritize scheduling meetings related to technology. Furthermore, if the user is in management roles, the scheduling unit can prioritize scheduling meetings related to management. This allows for improved meeting efficiency by applying scheduling methods tailored to the user's job content. Some or all of the above-described processes in the scheduling unit may be performed using, for example, a generating AI, or without one. For example, the scheduling unit can input data on the user's job content into a generating AI, which can then apply different scheduling methods.

[0045] The scheduling unit can prioritize scheduling meetings that are highly relevant, taking into account the user's geographical location during scheduling. For example, if the user is in a specific region, the scheduling unit will prioritize scheduling meetings related to that region. Furthermore, if the user is on a business trip, the scheduling unit can prioritize scheduling meetings related to their destination. Additionally, if the user is at home, the scheduling unit can prioritize scheduling meetings related to their home. This allows for the prioritization of highly relevant meetings by considering the user's geographical location, thereby improving meeting efficiency. Some or all of the above processing in the scheduling unit may be performed using, for example, a generative AI, or without one. For example, the scheduling unit can input the user's geographical location data into a generative AI, which can then prioritize scheduling highly relevant meetings.

[0046] The coordination unit can analyze the user's social media activity and coordinate relevant meetings during the coordination process. For example, the coordination unit can coordinate meetings related to topics that the user frequently mentions on social media. It can also coordinate meetings related to topics that the user's social media followers are interested in. Furthermore, the coordination unit can analyze the content of the user's social media posts and coordinate relevant meetings. This allows for the coordination of relevant meetings and improved meeting efficiency by analyzing the user's social media activity. Some or all of the above processing in the coordination unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the coordination unit can input data on the user's social media activity into a generative AI, which can then coordinate relevant meetings.

[0047] The prediction unit can analyze the user's past trouble history and select the optimal prediction method during prediction. For example, the prediction unit can select the optimal prediction method based on the user's past trouble history. The prediction unit can also propose an efficient prediction method based on the user's past trouble history. Furthermore, the prediction unit can analyze the user's past trouble history and select the most effective prediction method. This allows for the selection of the optimal prediction method by analyzing the user's past trouble history, thereby improving the accuracy of trouble prediction. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input data on the user's past trouble history into a generative AI, which can then select the optimal prediction method.

[0048] The prediction unit can apply different prediction methods depending on the user's job content during prediction. For example, if the user is in sales, the prediction unit will prioritize predicting sales-related problems. Similarly, if the user is in technical roles, the prediction unit can prioritize predicting technical problems. Furthermore, if the user is in management roles, the prediction unit can prioritize predicting management-related problems. This allows for improved accuracy in problem prediction by applying prediction methods tailored to the user's job content. Some or all of the above-described processes in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input data on the user's job content into a generative AI, which can then apply different prediction methods.

[0049] The prediction unit can prioritize predicting highly relevant problems by considering the user's geographical location information during prediction. For example, if the user is in a specific region, the prediction unit will prioritize predicting problems related to that region. Furthermore, if the user is on a business trip, the prediction unit can prioritize predicting problems related to the destination. Additionally, if the user is at home, the prediction unit can prioritize predicting problems related to their home. This allows for improved accuracy in problem prediction by prioritizing highly relevant problems based on the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the user's geographical location data into a generative AI, which can then prioritize predicting highly relevant problems.

[0050] The prediction unit can analyze the user's social media activity and predict related problems during the prediction process. For example, the prediction unit can predict problems related to topics that the user frequently mentions on social media. It can also predict problems related to topics that the user's social media followers are interested in. Furthermore, the prediction unit can analyze the content of the user's social media posts and predict related problems. This allows for the prediction of related problems by analyzing the user's social media activity, thereby improving the accuracy of problem prediction. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input data on the user's social media activity into a generative AI, which can then predict related problems.

[0051] The Private Management Unit can analyze a user's past private schedule history during management and select the optimal management method. For example, the Private Management Unit can select the optimal management method based on the user's frequently performed private schedules in the past. The Private Management Unit can also propose efficient management methods based on the user's past private schedule history. Furthermore, the Private Management Unit can analyze a user's past private schedule history and select the most effective management method. This allows for the selection of the optimal management method by analyzing the user's past private schedule history, thereby enriching their private life. Some or all of the above-described processes in the Private Management Unit may be performed using, for example, a generating AI, or without a generating AI. For example, the Private Management Unit can input data on the user's past private schedule history into a generating AI, which can then select the optimal management method.

[0052] The Private Management Unit can apply different management methods depending on the user's lifestyle during management. For example, if the user is busy, the Private Management Unit can apply a simple management method. If the user has more time, the Private Management Unit can also apply a more detailed management method. Furthermore, if the user is participating in a specific event, the Private Management Unit can apply a management method related to that event. This allows for the enrichment of the user's private life by applying a management method tailored to their lifestyle. Some or all of the above processing in the Private Management Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Private Management Unit can input data on the user's lifestyle into a generative AI, which can then apply different management methods.

[0053] The Private Management Unit can prioritize managing highly relevant appointments by considering the user's geographical location during management. For example, if the user is in a specific region, the Private Management Unit will prioritize appointments related to that region. Furthermore, if the user is traveling, the Private Management Unit can prioritize appointments related to their travel destination. Additionally, if the user is at home, the Private Management Unit can prioritize appointments related to their home. This allows for the prioritization of highly relevant appointments by considering the user's geographical location, thereby enhancing their private life. Some or all of the above processing in the Private Management Unit may be performed using, for example, a generative AI, or without one. For instance, the Private Management Unit can input the user's geographical location data into a generative AI, which can then prioritize managing highly relevant appointments.

[0054] The Private Management Unit can analyze a user's social media activity and manage related appointments during management. For example, the Private Management Unit can manage appointments related to events that the user frequently mentions on social media. It can also manage appointments related to events that the user's social media followers are interested in. Furthermore, the Private Management Unit can analyze the content of the user's social media posts and manage related appointments. In this way, by analyzing the user's social media activity, it is possible to manage related appointments and enrich their private life. Some or all of the above processing in the Private Management Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Private Management Unit can input data on the user's social media activity into a generative AI, and the generative AI can manage related appointments.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The agent AI system can also be equipped with a health management unit. This unit monitors the user's health status and provides appropriate advice. For example, it can analyze the user's sleep patterns and suggest the optimal amount of sleep. It can also record the user's diet and recommend a nutritionally balanced diet. Furthermore, it can track the user's exercise habits and provide an appropriate exercise plan. This allows for comprehensive management of the user's health status and supports a healthy lifestyle.

[0057] The agent AI system can also be equipped with a feedback unit. This feedback unit provides feedback on the user's work performance. For example, it can analyze the user's task completion rate and efficiency and suggest areas for improvement. It can also evaluate the user's communication skills and advise on effective communication methods. Furthermore, it can monitor the user's stress level and provide advice for stress reduction. This can improve the user's work performance and reduce stress.

[0058] The agent AI system can also be equipped with a learning support unit. This unit supports the user's skill development. For example, it can create learning plans for users to acquire new skills. It can also monitor the user's learning progress and provide appropriate feedback. Furthermore, it can provide learning materials tailored to the user's learning style. This effectively supports the user's skill development and promotes career advancement.

[0059] The agent AI system can also be equipped with a reminder function. This reminder function will remind users of important tasks and events. For example, it can send reminders for meetings and deadlines based on the user's schedule. It can also remind users of their personal appointments. Furthermore, it can provide reminders related to the user's health management. This helps users remember and complete important tasks and events.

[0060] The agent AI system can also be equipped with a motivation function. This function provides support to maintain and improve user motivation. For example, it visualizes the user's progress toward achieving their goals, providing a sense of accomplishment. It can also recognize the user's efforts and provide appropriate feedback. Furthermore, it can offer encouragement and advice to boost user motivation. This helps maintain user motivation and improve the efficiency of work performance.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The learning unit learns behavioral and work patterns. For example, it learns by inputting the behavioral patterns, data created, and information transmission methods (email, messaging apps, phone calls, etc.) of each business person. Step 2: The coordination unit performs meeting scheduling and task management based on the data learned by the learning unit. For example, it handles tedious tasks such as meeting scheduling, preparation, follow-up, and meeting member selection. Step 3: The Forecasting Department predicts and shares unexpected problems based on meetings and tasks coordinated by the Coordination Department. For example, it predicts system failures, human errors, and problems caused by external factors, and shares this information with business personnel. Step 4: The Private Management Department manages private schedules based on the troubles predicted by the Forecasting Department. For example, they manage private schedules such as weekend events, family activities, and local social activities.

[0063] (Example of form 2) The agent AI system according to an embodiment of the present invention is a system designed to solve problems faced by Japanese business people, such as long working hours and overwork, work-life balance, career stagnation, and future anxieties. This agent AI system takes all of each business person's behavioral patterns, created data, and information transmission (email, messaging apps, phone calls, etc.) as input and the AI ​​learns from it. Next, based on the data the AI ​​has learned, it coordinates all of the troublesome tasks of meeting scheduling, preparation, follow-up, and meeting member selection. It also shares predicted problems and events with business people in advance, before unexpected troubles occur. The final decision is made by each business person, but the AI's support allows them to create more time for themselves and relieve stress. Furthermore, the AI ​​learns the behavioral and work patterns of business people and provides an optimal environment. For example, it checks work tasks, cross-checks for missed tasks and human errors, detects problems in advance, and adjusts schedules. In this way, it improves the stress levels of business people and helps them regain their happiness. This AI agent system not only manages office work but also optimally coordinates personal concerns. For example, it manages personal schedules such as weekend events, family time, and local social activities, supporting business professionals so they can do what they truly want to do, experience what they want to experience, and focus on what they want to do. In this way, the AI ​​agent system can solve business professionals' problems and enable them to perform their work efficiently while also enriching their personal lives.

[0064] The agent AI system according to this embodiment comprises a learning unit, a coordination unit, a prediction unit, and a private management unit. The learning unit learns behavioral patterns and work patterns. For example, the learning unit learns by inputting the behavioral patterns, created data, and information transmission (email, messaging apps, phone calls, etc.) of each business person. The coordination unit performs meeting coordination and task management based on the data learned by the learning unit. For example, the coordination unit handles tedious meeting coordination, preparation, follow-up, and meeting member coordination. The prediction unit predicts and shares unexpected troubles based on the meetings and tasks coordinated by the coordination unit. For example, the prediction unit predicts troubles such as system failures, human errors, and troubles caused by external factors, and shares them with business people. The private management unit manages private schedules based on troubles predicted by the prediction unit. For example, the private management unit manages private schedules such as weekend events, family activities, and local social activities. This allows the agent AI system to learn the behavioral and work patterns of business professionals, enabling efficient work execution and a fulfilling personal life by handling meeting scheduling, task management, trouble prediction, and personal schedule management.

[0065] The learning unit learns behavioral and work patterns. For example, it learns by inputting each business person's behavioral patterns, created data, and information transmission (email, messaging apps, phone calls, etc.). Specifically, it collects the history of daily tasks and communications performed by business people and analyzes behavioral patterns based on this data. For example, it collects email sending and receiving history, messaging app chat logs, and phone call records, and analyzes this data using natural language processing technology. Furthermore, it collects creation data such as documents, spreadsheets, and presentation materials created by business people, and extracts work patterns from this data. Using this data, the learning unit learns the workflow, priorities, and frequently performed tasks of business people, building a foundation for providing optimal support to individual business people. Moreover, the learning unit uses machine learning algorithms to continuously learn changes in business people's behavioral and work patterns, enabling it to provide support based on the latest information. For example, even if a new project starts or work content changes, the learning unit can respond quickly and provide support tailored to the needs of business people. This allows the learning department to play a crucial role in improving the work efficiency of business professionals.

[0066] The Coordination Unit performs meeting scheduling and task management based on data learned by the Learning Unit. For example, the Coordination Unit handles tedious tasks such as meeting scheduling, preparation, follow-up, and coordinating meeting members. Specifically, it proposes optimal meeting schedules based on the behavioral and work patterns of business professionals collected by the Learning Unit. For instance, it automatically detects each member's availability and finds a time slot where everyone can participate. It also prepares necessary materials and information in advance according to the meeting's purpose and agenda, supporting the smooth progress of the meeting. Furthermore, as a post-meeting follow-up, it automates the creation of meeting minutes and task assignment, notifying each member of appropriate tasks. Through these functions, the Coordination Unit significantly reduces the time business professionals spend on meeting scheduling and task management, allowing them to focus on more important tasks. The Coordination Unit also monitors task progress in real time and automatically reminds users of tasks with approaching deadlines or those that are incomplete. This prevents tasks from being missed or delayed, enabling business professionals to work efficiently. Furthermore, the coordination unit supports business professionals in focusing on their most important tasks by centrally managing and prioritizing multiple projects and tasks. This allows the coordination unit to play a crucial role in improving the work efficiency of business professionals.

[0067] The Forecasting Department predicts and shares potential problems based on meetings and tasks coordinated by the Coordination Department. For example, the Forecasting Department predicts system failures, human error, and problems caused by external factors, and shares this information with business personnel. Specifically, it uses historical data and statistical information, based on meeting schedules and task progress managed by the Coordination Department, to predict the probability of unexpected problems occurring. For example, it analyzes past meeting data to identify problems that are likely to occur under specific conditions. It also monitors task progress to predict the possibility of delays or incomplete tasks. Furthermore, it considers external factors such as weather information, traffic information, and economic trends, and predicts the impact these factors will have on business operations. The Forecasting Department shares these prediction results with business personnel in real time, providing information to enable them to take preventative measures. For example, if there is a high probability of a system failure, it notifies them to prepare a backup system. If there is a possibility of human error, it instructs them to be vigilant and perform additional checks. This allows the Forecasting Department to enable business personnel to respond quickly to unexpected problems, minimizing business interruptions and delays. Furthermore, the forecasting unit contributes to long-term risk management and the development of countermeasures based on the forecast results. For example, if a particular problem occurs frequently, identifying its cause and implementing fundamental countermeasures can reduce future risks. In this way, the forecasting unit can play an important role in improving the work efficiency of business people.

[0068] The Private Management Department manages personal schedules based on troubles predicted by the Forecasting Department. For example, it manages personal schedules such as weekend events, family activities, and local social engagements. Specifically, it centrally manages business professionals' personal schedules and identifies factors that may affect them based on trouble prediction information provided by the Forecasting Department. For instance, if a family event scheduled for the weekend may be affected by a predicted system failure or external factor, the Private Management Department will notify the business professional in advance and suggest alternative plans. Furthermore, if a business professional's personal schedule may impact work, such as an approaching deadline for an important meeting or task, the Private Management Department will suggest appropriate adjustments to the business professional. In addition, the Private Management Department has a function to optimize work schedules while considering business professionals' personal schedules. For example, it adjusts work schedules to ensure personal time is available to prioritize family time. The Private Management Department also contributes to the health and stress management of business professionals. For example, it suggests regular rest and refreshment time to support business professionals in maintaining a healthy lifestyle. This allows the private management department to play a crucial role in optimizing the work-life balance of business professionals and improving their overall quality of life.

[0069] The learning unit can learn by inputting the behavioral patterns, created data, and information transmission of each business person. For example, the learning unit can learn daily actions, actions during work, and actions under specific circumstances. The learning unit can also learn created data such as document data, spreadsheets, and presentation materials. Furthermore, the learning unit can learn communication methods such as email, chat, and telephone. This allows the learning unit to provide individually optimized support by learning the behavioral patterns, created data, and information transmission of each business person. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the behavioral patterns and created data of each business person into a generative AI, which can then perform the learning.

[0070] The coordination unit can perform tedious tasks such as meeting scheduling, preparation, follow-up, and meeting member selection based on data learned by the learning unit. For example, the coordination unit can schedule meetings, select participants, and reserve meeting rooms. It can also prepare materials, set up meeting rooms, and notify participants. Furthermore, it can perform follow-up tasks such as creating meeting minutes, reviewing action items, and scheduling follow-up meetings. This automates the tedious tasks of meeting scheduling, preparation, follow-up, and meeting member selection, thereby reducing the burden on business professionals. Some or all of the above-described processes in the coordination unit may be performed using, for example, a generative AI, or not. For instance, the coordination unit can input data learned by the learning unit into a generative AI, which can then perform meeting scheduling and task management.

[0071] The prediction unit can predict unexpected problems based on meetings and tasks coordinated by the coordination unit and share this information with business personnel. For example, the prediction unit can predict system failures, human errors, and problems caused by external factors. The prediction unit can also calculate the probability of a problem occurring and share this information with business personnel. Furthermore, the prediction unit can evaluate the scope of the problem's impact and share this information with business personnel. This allows for the prevention of problems by predicting unexpected issues and sharing them with business personnel in advance. Some or all of the above-described processes in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input data on meetings and tasks coordinated by the coordination unit into a generative AI, which can then perform the problem prediction.

[0072] The Private Management Department can manage private appointments such as weekend events, family activities, and local social activities based on troubles predicted by the Prediction Department. For example, the Private Management Department manages weekend events such as outings with family, hobby activities, and gatherings with friends. It can also manage family activities such as spending time with family, helping with household chores, and childcare. Furthermore, the Private Management Department can manage local social activities such as participating in community events and interacting with neighbors. In this way, by managing private appointments, it is possible to support business people in being able to do what they really want to do, experience what they want to experience, and focus on what they want to do. Some or all of the above processes in the Private Management Department may be performed using, for example, a Generative AI, or not using a Generative AI. For example, the Private Management Department can input trouble data predicted by the Prediction Department into a Generative AI, and the Generative AI can manage private appointments.

[0073] The adjustment unit can check work tasks, perform cross-checks for missed tasks and human errors, detect problems in advance, and adjust schedules. For example, the adjustment unit checks work tasks such as confirming task progress and completion status. The adjustment unit can also detect missed tasks, such as incomplete tasks and forgotten tasks. Furthermore, the adjustment unit can perform cross-checks for human errors, such as verification by multiple people and the use of error checklists. In this way, by checking work tasks, performing cross-checks for missed tasks and human errors, problems can be detected in advance and schedules can be adjusted. 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 work task data into a generation AI, which can then detect problems and adjust schedules.

[0074] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit will prioritize learning data that promotes relaxation. Furthermore, if the user is focused, the learning unit can learn data related to challenging tasks. Additionally, if the user is tired, the learning unit can learn data related to easy tasks. This allows for more effective learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or not. For example, the learning unit can input user emotion data into a generative AI, which can then select the training data.

[0075] The learning unit can analyze the user's past behavior patterns during learning and apply the optimal learning algorithm. For example, the learning unit can select the optimal learning algorithm based on tasks the user has frequently performed in the past. The learning unit can also suggest efficient learning methods based on the user's past behavior patterns. Furthermore, the learning unit can analyze the user's past behavior history and apply the most effective learning algorithm. This allows for the application of the optimal learning algorithm by analyzing the user's past behavior patterns, thereby improving learning efficiency. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input data on the user's past behavior patterns into a generative AI, which can then apply the optimal learning algorithm.

[0076] The learning unit can apply different learning methods depending on the user's job content during the learning process. For example, if the user is in sales, the learning unit will prioritize learning data related to sales. Similarly, if the user is in a technical position, the learning unit can prioritize learning data related to technology. Furthermore, if the user is in a management position, the learning unit can prioritize learning data related to management. This maximizes the effectiveness of learning by applying learning methods tailored to the user's job content. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input data on the user's job content into a generative AI, which can then apply different learning methods.

[0077] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency to alleviate the burden. Conversely, if the user is relaxed, the learning unit can increase the learning frequency to improve efficiency. Furthermore, if the user is focused, the learning unit can optimize the learning frequency to maximize effectiveness. In this way, by adjusting the learning frequency based on the user's emotions, the learning burden can be reduced and efficiency can be improved. 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 learning unit may be performed using a generative AI, or not using a generative AI. For example, the learning unit can input user emotion data into a generative AI, which can then adjust the learning frequency.

[0078] The learning unit can prioritize learning highly relevant data by considering the user's geographical location during the learning process. For example, if the user is in a specific region, the learning unit will prioritize learning data related to that region. Furthermore, if the user is on a business trip, the learning unit can prioritize learning data related to the destination. Additionally, if the user is at home, the learning unit can prioritize learning data related to their home. This allows for the prioritization of highly relevant data and enhances the effectiveness of the learning process by considering the user's geographical location. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's geographical location data into a generative AI, which can then prioritize learning highly relevant data.

[0079] The learning unit can analyze the user's social media activity and learn relevant data during the learning process. For example, the learning unit can learn data related to topics that the user frequently mentions on social media. It can also learn data related to topics that the user's social media followers are interested in. Furthermore, the learning unit can analyze the content of the user's social media posts and learn relevant data. This allows the learning unit to learn relevant data by analyzing the user's social media activity and improve the accuracy of the learning process. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input data on the user's social media activity into a generative AI, which can then learn the relevant data.

[0080] The adjustment unit can estimate the user's emotions and adjust the meeting scheduling method based on the estimated user emotions. For example, if the user is stressed, the adjustment unit can reduce the frequency of meetings to alleviate the burden. Conversely, if the user is relaxed, the adjustment unit can increase the frequency of meetings to improve efficiency. Furthermore, if the user is focused, the adjustment unit can optimize the meeting time to maximize its effectiveness. In this way, the efficiency of meetings can be improved by adjusting the meeting scheduling method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 adjust the meeting scheduling method.

[0081] The scheduling unit can analyze the user's past meeting history and select the optimal scheduling method during scheduling. For example, the scheduling unit can propose the optimal meeting schedule based on the user's past meeting history. It can also propose efficient meeting scheduling methods based on the user's past meeting history. Furthermore, the scheduling unit can analyze the user's past meeting history and select the most effective scheduling method. This allows for the selection of the optimal scheduling method by analyzing the user's past meeting history, thereby improving meeting efficiency. Some or all of the above processing in the scheduling unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scheduling unit can input data on the user's past meeting history into a generative AI, which can then select the optimal scheduling method.

[0082] The scheduling unit can apply different scheduling methods depending on the user's job content during scheduling. For example, if the user is in sales, the scheduling unit will prioritize scheduling meetings related to sales. Similarly, if the user is in technical roles, the scheduling unit can prioritize scheduling meetings related to technology. Furthermore, if the user is in management roles, the scheduling unit can prioritize scheduling meetings related to management. This allows for improved meeting efficiency by applying scheduling methods tailored to the user's job content. Some or all of the above-described processes in the scheduling unit may be performed using, for example, a generating AI, or without one. For example, the scheduling unit can input data on the user's job content into a generating AI, which can then apply different scheduling methods.

[0083] The scheduling unit can estimate the user's emotions and determine meeting priorities based on those emotions. For example, if the user is stressed, the scheduling unit may postpone less important meetings. Conversely, if the user is relaxed, the scheduling unit may prioritize more important meetings. Furthermore, if the user is focused, the scheduling unit may prioritize more important meetings. This allows for the prioritization of important meetings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using or without a generative AI. For example, the scheduling unit can input user emotion data into a generative AI, which can then determine meeting priorities.

[0084] The scheduling unit can prioritize scheduling meetings that are highly relevant, taking into account the user's geographical location during scheduling. For example, if the user is in a specific region, the scheduling unit will prioritize scheduling meetings related to that region. Furthermore, if the user is on a business trip, the scheduling unit can prioritize scheduling meetings related to their destination. Additionally, if the user is at home, the scheduling unit can prioritize scheduling meetings related to their home. This allows for the prioritization of highly relevant meetings by considering the user's geographical location, thereby improving meeting efficiency. Some or all of the above processing in the scheduling unit may be performed using, for example, a generative AI, or without one. For example, the scheduling unit can input the user's geographical location data into a generative AI, which can then prioritize scheduling highly relevant meetings.

[0085] The coordination unit can analyze the user's social media activity and coordinate relevant meetings during the coordination process. For example, the coordination unit can coordinate meetings related to topics that the user frequently mentions on social media. It can also coordinate meetings related to topics that the user's social media followers are interested in. Furthermore, the coordination unit can analyze the content of the user's social media posts and coordinate relevant meetings. This allows for the coordination of relevant meetings and improved meeting efficiency by analyzing the user's social media activity. Some or all of the above processing in the coordination unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the coordination unit can input data on the user's social media activity into a generative AI, which can then coordinate relevant meetings.

[0086] The prediction unit can estimate the user's emotions and adjust the trouble prediction method based on the estimated user emotions. For example, if the user is stressed, the prediction unit can reduce the frequency of trouble predictions to alleviate the burden. Conversely, if the user is relaxed, the prediction unit can increase the frequency of trouble predictions to improve efficiency. Furthermore, if the user is focused, the prediction unit can optimize the trouble prediction method to maximize its effectiveness. In this way, the efficiency of trouble prediction can be improved by adjusting the trouble prediction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 prediction unit may be performed using a generative AI, or not using a generative AI. For example, the prediction unit can input user emotion data into a generative AI, which can then adjust the trouble prediction method.

[0087] The prediction unit can analyze the user's past trouble history and select the optimal prediction method during prediction. For example, the prediction unit can select the optimal prediction method based on the user's past trouble history. The prediction unit can also propose an efficient prediction method based on the user's past trouble history. Furthermore, the prediction unit can analyze the user's past trouble history and select the most effective prediction method. This allows for the selection of the optimal prediction method by analyzing the user's past trouble history, thereby improving the accuracy of trouble prediction. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input data on the user's past trouble history into a generative AI, which can then select the optimal prediction method.

[0088] The prediction unit can apply different prediction methods depending on the user's job content during prediction. For example, if the user is in sales, the prediction unit will prioritize predicting sales-related problems. Similarly, if the user is in technical roles, the prediction unit can prioritize predicting technical problems. Furthermore, if the user is in management roles, the prediction unit can prioritize predicting management-related problems. This allows for improved accuracy in problem prediction by applying prediction methods tailored to the user's job content. Some or all of the above-described processes in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input data on the user's job content into a generative AI, which can then apply different prediction methods.

[0089] The prediction unit can estimate the user's emotions and determine the priority of problems based on the estimated emotions. For example, if the user is stressed, the prediction unit may postpone less important problems. Conversely, if the user is relaxed, the prediction unit may prioritize more important problems. Furthermore, if the user is focused, the prediction unit may prioritize more important problems. This allows for the prioritization of important problems by determining the priority of problems based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the prediction unit can input user emotion data into a generative AI, which can then determine the priority of problems.

[0090] The prediction unit can prioritize predicting highly relevant problems by considering the user's geographical location information during prediction. For example, if the user is in a specific region, the prediction unit will prioritize predicting problems related to that region. Furthermore, if the user is on a business trip, the prediction unit can prioritize predicting problems related to the destination. Additionally, if the user is at home, the prediction unit can prioritize predicting problems related to their home. This allows for improved accuracy in problem prediction by prioritizing highly relevant problems based on the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the user's geographical location data into a generative AI, which can then prioritize predicting highly relevant problems.

[0091] The prediction unit can analyze the user's social media activity and predict related problems during the prediction process. For example, the prediction unit can predict problems related to topics that the user frequently mentions on social media. It can also predict problems related to topics that the user's social media followers are interested in. Furthermore, the prediction unit can analyze the content of the user's social media posts and predict related problems. This allows for the prediction of related problems by analyzing the user's social media activity, thereby improving the accuracy of problem prediction. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input data on the user's social media activity into a generative AI, which can then predict related problems.

[0092] The private management unit can estimate the user's emotions and adjust the method of managing private schedules based on the estimated emotions. For example, if the user is feeling stressed, the private management unit can prioritize relaxing schedules. It can also prioritize active schedules if the user is relaxed. Furthermore, if the user is focused, the private management unit can prioritize important schedules. This allows for a more fulfilling private life by adjusting the method of managing private schedules based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the private management unit may be performed using or without a generative AI. For example, the private management unit can input user emotion data into a generative AI, which can then adjust the method of managing private schedules.

[0093] The Private Management Unit can analyze a user's past private schedule history during management and select the optimal management method. For example, the Private Management Unit can select the optimal management method based on the user's frequently performed private schedules in the past. The Private Management Unit can also propose efficient management methods based on the user's past private schedule history. Furthermore, the Private Management Unit can analyze a user's past private schedule history and select the most effective management method. This allows for the selection of the optimal management method by analyzing the user's past private schedule history, thereby enriching their private life. Some or all of the above-described processes in the Private Management Unit may be performed using, for example, a generating AI, or without a generating AI. For example, the Private Management Unit can input data on the user's past private schedule history into a generating AI, which can then select the optimal management method.

[0094] The Private Management Unit can apply different management methods depending on the user's lifestyle during management. For example, if the user is busy, the Private Management Unit can apply a simple management method. If the user has more time, the Private Management Unit can also apply a more detailed management method. Furthermore, if the user is participating in a specific event, the Private Management Unit can apply a management method related to that event. This allows for the enrichment of the user's private life by applying a management method tailored to their lifestyle. Some or all of the above processing in the Private Management Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Private Management Unit can input data on the user's lifestyle into a generative AI, which can then apply different management methods.

[0095] The private management unit can estimate the user's emotions and prioritize private appointments based on those emotions. For example, if the user is stressed, the private management unit will prioritize relaxing appointments. If the user is relaxed, the private management unit can also prioritize active appointments. Furthermore, if the user is focused, the private management unit can prioritize important appointments. This allows for the priority management of important appointments by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the private management unit may be performed using or without a generative AI. For example, the private management unit can input user emotion data into a generative AI, which can then determine the priority of private appointments.

[0096] The Private Management Unit can prioritize managing highly relevant appointments by considering the user's geographical location during management. For example, if the user is in a specific region, the Private Management Unit will prioritize appointments related to that region. Furthermore, if the user is traveling, the Private Management Unit can prioritize appointments related to their travel destination. Additionally, if the user is at home, the Private Management Unit can prioritize appointments related to their home. This allows for the prioritization of highly relevant appointments by considering the user's geographical location, thereby enhancing their private life. Some or all of the above processing in the Private Management Unit may be performed using, for example, a generative AI, or without one. For instance, the Private Management Unit can input the user's geographical location data into a generative AI, which can then prioritize managing highly relevant appointments.

[0097] The Private Management Unit can analyze a user's social media activity and manage related appointments during management. For example, the Private Management Unit can manage appointments related to events that the user frequently mentions on social media. It can also manage appointments related to events that the user's social media followers are interested in. Furthermore, the Private Management Unit can analyze the content of the user's social media posts and manage related appointments. In this way, by analyzing the user's social media activity, it is possible to manage related appointments and enrich their private life. Some or all of the above processing in the Private Management Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Private Management Unit can input data on the user's social media activity into a generative AI, and the generative AI can manage related appointments.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The agent AI system can also be equipped with a health management unit. This unit monitors the user's health status and provides appropriate advice. For example, it can analyze the user's sleep patterns and suggest the optimal amount of sleep. It can also record the user's diet and recommend a nutritionally balanced diet. Furthermore, it can track the user's exercise habits and provide an appropriate exercise plan. This allows for comprehensive management of the user's health status and supports a healthy lifestyle.

[0100] The agent AI system can also be equipped with a feedback unit. This feedback unit provides feedback on the user's work performance. For example, it can analyze the user's task completion rate and efficiency and suggest areas for improvement. It can also evaluate the user's communication skills and advise on effective communication methods. Furthermore, it can monitor the user's stress level and provide advice for stress reduction. This can improve the user's work performance and reduce stress.

[0101] The agent AI system can also be equipped with a learning support unit. This unit supports the user's skill development. For example, it can create learning plans for users to acquire new skills. It can also monitor the user's learning progress and provide appropriate feedback. Furthermore, it can provide learning materials tailored to the user's learning style. This effectively supports the user's skill development and promotes career advancement.

[0102] The agent AI system can also be equipped with a reminder function. This reminder function will remind users of important tasks and events. For example, it can send reminders for meetings and deadlines based on the user's schedule. It can also remind users of their personal appointments. Furthermore, it can provide reminders related to the user's health management. This helps users remember and complete important tasks and events.

[0103] The agent AI system can also be equipped with a motivation function. This function provides support to maintain and improve user motivation. For example, it visualizes the user's progress toward achieving their goals, providing a sense of accomplishment. It can also recognize the user's efforts and provide appropriate feedback. Furthermore, it can offer encouragement and advice to boost user motivation. This helps maintain user motivation and improve the efficiency of work performance.

[0104] The agent AI system can estimate the user's emotions and suggest relaxation methods based on those estimates. For example, if the user is feeling stressed, it can suggest relaxing music or meditation. If the user is tired, it can suggest short breaks or stretching. Furthermore, if the user is feeling anxious, it can suggest relaxing activities or ways to refresh themselves. This allows the system to provide relaxation methods tailored to the user's emotions and reduce stress.

[0105] The agent AI system can estimate a user's emotions and adjust its communication style based on those emotions. For example, if a user is stressed, it may recommend concise and clear communication. If the user is relaxed, it can provide more detailed information. Furthermore, if the user is focused, it can prioritize conveying important information. This allows for communication tailored to the user's emotions, resulting in effective information transmission.

[0106] The agent AI system can estimate the user's emotions and adjust task priorities based on those estimates. For example, if the user is stressed, it can postpone less important tasks. Conversely, if the user is relaxed, it can prioritize more important tasks. Furthermore, if the user is focused, it can prioritize more difficult tasks. This provides task priorities tailored to the user's emotions, supporting efficient work performance.

[0107] The agent AI system can estimate a user's emotions and suggest break times based on those estimates. For example, if a user is feeling stressed, it can suggest a short break. If a user is tired, it can suggest a refreshing break. Furthermore, if a user is concentrating, it can suggest a break at an appropriate time. This allows for breaks tailored to the user's emotions, thereby improving work efficiency.

[0108] The agent AI system can estimate the user's emotions and adjust the content of feedback based on those emotions. For example, if the user is stressed, it will prioritize positive feedback. If the user is relaxed, it can provide constructive feedback. Furthermore, if the user is focused, it can suggest specific areas for improvement. This allows for feedback tailored to the user's emotions, improving the efficiency of work performance.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The learning unit learns behavioral and work patterns. For example, it learns by inputting the behavioral patterns, data created, and information transmission methods (email, messaging apps, phone calls, etc.) of each business person. Step 2: The coordination unit performs meeting scheduling and task management based on the data learned by the learning unit. For example, it handles tedious tasks such as meeting scheduling, preparation, follow-up, and meeting member selection. Step 3: The Forecasting Department predicts and shares unexpected problems based on meetings and tasks coordinated by the Coordination Department. For example, it predicts system failures, human errors, and problems caused by external factors, and shares this information with business personnel. Step 4: The Private Management Department manages private schedules based on the troubles predicted by the Forecasting Department. For example, they manage private schedules such as weekend events, family activities, and local social activities.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the learning unit, adjustment unit, prediction unit, and private management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 and learns by inputting the behavioral patterns, created data, and information transmission of business people. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs meeting adjustments and task management based on the learned data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts and shares unexpected troubles based on the adjusted meetings and tasks. The private management unit is implemented by the control unit 46A of the smart device 14 and manages private schedules based on the predicted troubles. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the learning unit, adjustment unit, prediction unit, and private management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 and learns by inputting the behavioral patterns, created data, and information transmission of business people. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs meeting adjustments and task management based on the learned data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts and shares unexpected troubles based on the adjusted meetings and tasks. The private management unit is implemented by the control unit 46A of the smart glasses 214 and manages private schedules based on the predicted troubles. 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] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0146] Each of the multiple elements described above, including the learning unit, adjustment unit, prediction unit, and private management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 and learns by inputting the behavioral patterns, created data, and information transmission of business people. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs meeting adjustments and task management based on the learned data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts and shares unexpected troubles based on the adjusted meetings and tasks. The private management unit is implemented by the control unit 46A of the headset terminal 314 and manages private schedules based on the predicted troubles. 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] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[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 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.

[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 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).

[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] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the learning unit, adjustment unit, prediction unit, and private management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 and learns by inputting the behavioral patterns, created data, and information transmission of business people. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs meeting adjustments and task management based on the learned data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts and shares unexpected troubles based on the adjusted meetings and tasks. The private management unit is implemented by the control unit 46A of the robot 414 and manages private schedules based on the predicted troubles. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A learning unit that learns behavioral patterns and work patterns, A coordination unit that performs meeting coordination and task management based on data learned by the aforementioned learning unit, A prediction unit predicts and shares unexpected troubles based on meetings and tasks coordinated by the aforementioned coordination unit, The system includes a private management unit that manages private schedules based on troubles predicted by the aforementioned prediction unit. A system characterized by the following features. (Note 2) The aforementioned learning unit, The system learns by inputting the behavioral patterns, data created, and information transmission methods of each business person. The system described in Appendix 1, characterized by the features described herein. (Note 3) The adjustment unit is, Based on the data learned by the aforementioned learning unit, the cumbersome tasks of meeting scheduling, preparation, follow-up, and meeting member selection are performed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Based on the meetings and tasks coordinated by the aforementioned coordination unit, unexpected problems are predicted and shared with business personnel. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Private Management Department Based on the troubles predicted by the aforementioned prediction unit, the system manages private plans such as weekend events, family activities, and local social engagements. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, We perform cross-checks to identify work tasks, prevent omissions, and avoid human errors, thereby detecting problems in advance and adjusting schedules accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, During training, the system analyzes the user's past behavior patterns and applies the optimal learning algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, During training, different learning methods are applied depending on the user's work content. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During training, the system prioritizes learning highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, During training, the system analyzes users' social media activity and learns from relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The adjustment unit is, It estimates the user's emotions and adjusts the meeting scheduling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The adjustment unit is, During scheduling, the system analyzes the user's past meeting history to select the most suitable scheduling method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The adjustment unit is, During the adjustment process, different adjustment methods are applied depending on the user's work content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The adjustment unit is, It estimates user emotions and determines meeting priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, During scheduling, the system prioritizes scheduling highly relevant meetings by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, During the scheduling process, we analyze users' social media activity and coordinate relevant meetings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, We estimate the user's emotions and adjust the trouble prediction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, During the prediction process, the system analyzes the user's past trouble history and selects the optimal prediction method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, different prediction methods are applied depending on the user's business content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, It estimates the user's emotions and determines the priority of issues based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, the system prioritizes predicting highly relevant problems by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, During the prediction process, we analyze users' social media activity and predict related problems. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Private Management Department It estimates the user's emotions and adjusts how they manage their private schedule based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Private Management Department During management, the system analyzes the user's past private schedule history and selects the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Private Management Department During management, different management methods are applied depending on the user's living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Private Management Department It estimates the user's emotions and prioritizes private appointments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Private Management Department During management, the system prioritizes managing appointments that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Private Management Department During administration, analyze users' social media activity and manage related appointments. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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 learning unit that learns behavioral patterns and work patterns, A coordination unit that performs meeting coordination and task management based on data learned by the aforementioned learning unit, A prediction unit predicts and shares unexpected troubles based on meetings and tasks coordinated by the aforementioned coordination unit, The system includes a private management unit that manages private schedules based on troubles predicted by the aforementioned prediction unit. A system characterized by the following features.

2. The aforementioned learning unit, The system learns by inputting the behavioral patterns, data created, and information transmission methods of each business person. The system according to feature 1.

3. The adjustment unit is, Based on the data learned by the aforementioned learning unit, the cumbersome tasks of meeting scheduling, preparation, follow-up, and meeting member selection are performed. The system according to feature 1.

4. The prediction unit, Based on the meetings and tasks coordinated by the aforementioned coordination unit, unexpected problems are predicted and shared with business personnel. The system according to feature 1.

5. The aforementioned Private Management Department Based on the troubles predicted by the aforementioned prediction unit, the system manages private plans such as weekend events, family activities, and local social engagements. The system according to feature 1.

6. The adjustment unit is, We perform cross-checks to identify work tasks, prevent omissions, and avoid human errors, thereby detecting problems in advance and adjusting schedules accordingly. The system according to feature 1.

7. The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system according to feature 1.

8. The aforementioned learning unit, During training, the system analyzes the user's past behavior patterns and applies the optimal learning algorithm. The system according to feature 1.

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