system

Abstract

The system according to this embodiment aims to improve work efficiency by automating meeting scheduling, task assignment, and progress management. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a schedule setting unit, a task assignment unit, an automation unit, and a notification unit. The reception unit receives voice input. The analysis unit analyzes the voice instructions received by the reception unit. The schedule setting unit sets the meeting schedule based on the instructions analyzed by the analysis unit. The task assignment unit assigns tasks based on the instructions analyzed by the analysis unit. The automation unit automates the progress management of schedules and tasks set by the schedule setting unit and the task assignment unit. The notification unit sends reminders and notifications for schedules and tasks managed by the automation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is necessary to manually set the schedule of meetings, assign tasks, and manage progress, and there is a limit to improving work efficiency.

[0005] The system according to the embodiment aims to automate the schedule setting of meetings, task assignment, and progress management, and improve work efficiency.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a reception unit, an analysis unit, a schedule setting unit, a task assignment unit, an automation unit, and a notification unit. The reception unit receives voice input. The analysis unit analyzes the voice instructions received by the reception unit. The schedule setting unit sets the meeting schedule based on the instructions analyzed by the analysis unit. The task assignment unit assigns tasks based on the instructions analyzed by the analysis unit. The automation unit automates the progress management of schedules and tasks set by the schedule setting unit and the task assignment unit. The notification unit sends reminders and notifications for schedules and tasks managed by the automation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate meeting scheduling, task assignment, and progress management, thereby improving work efficiency. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 business support system according to an embodiment of the present invention is an AI-based business support platform developed for business owners and team managers of small and medium-sized enterprises. This business support system maximizes productivity by simplifying business processes and automating tasks. The business support system is equipped with a voice input function, making it easy to set and change meeting schedules and assign tasks. The business support system supports improved work efficiency and reduced stress, enabling smoother business operations. First, the user gives instructions to set and change meeting schedules and assign tasks via voice input. For example, the user gives a voice command such as, "Schedule a meeting for 10 AM tomorrow." This voice command is input into the business support system. Next, the business support system analyzes the input voice command and takes appropriate action. For example, when setting a meeting schedule, the business support system checks the participants' schedules and suggests the optimal time. Also, when assigning tasks, the business support system considers the workload of each member and assigns tasks to the appropriate members. Furthermore, the business support system automates business processes. For example, it reduces the user's workload by automating the setting of regular meeting schedules and the management of task progress. Furthermore, it prioritizes tasks and sends reminders and notifications for important tasks. This system allows users to improve work efficiency. For example, automating meeting scheduling prevents double bookings and missed appointments. Automated task progress management prevents tasks from being overlooked or delayed. Business support systems help improve work efficiency and reduce stress. Users can simplify cumbersome tasks, saving time and energy. For example, scheduling meetings and assigning tasks can be easily done using voice input, reducing the workload. Also, automating business processes allows users to focus on important tasks. In this way, business support systems are a powerful tool for small and medium-sized business owners and team managers to improve work efficiency and reduce stress.This allows business support systems to streamline users' work and reduce stress.

[0029] The business support system according to this embodiment comprises a reception unit, an analysis unit, a schedule setting unit, a task assignment unit, an automation unit, and a notification unit. The reception unit receives voice input. The reception unit can receive voice input using, for example, a microphone. The reception unit can also receive voice input using specific speech recognition technology. For example, the reception unit can convert voice input to text using a speech recognition algorithm. The analysis unit converts voice instructions to text and identifies appropriate actions. The analysis unit can convert voice instructions to text using, for example, a speech recognition algorithm. The analysis unit can also analyze voice instructions using natural language processing technology and identify appropriate actions. For example, the analysis unit analyzes the content of voice instructions and identifies actions such as scheduling meetings or assigning tasks. The schedule setting unit schedules meetings based on the instructions analyzed by the analysis unit. The schedule setting unit can, for example, check participants' schedules and suggest the optimal time. The schedule setting unit can also link with a calendar application to automatically set schedules. For example, the scheduling unit checks participants' availability and proposes the optimal meeting time. The task assignment unit assigns tasks based on instructions analyzed by the analysis unit. The task assignment unit can, for example, consider each member's workload and assign tasks to the appropriate member. It can also consider members' skill sets and assign tasks to the most suitable member. For example, the task assignment unit evaluates members' workloads and assigns appropriate tasks. The automation unit automates the progress management of schedules and tasks set by the scheduling and task assignment units. The automation unit can, for example, automate the setting of regular schedules and the progress management of tasks. It can also prioritize tasks and send reminders and notifications for important tasks. For example, the automation unit can automate the scheduling of regular meetings to prevent double bookings and missed appointments.The notification unit sends reminders and notifications for schedules and tasks managed by the automation unit. For example, the notification unit can send reminders for important tasks. It can also send notifications according to the progress of tasks. For example, the notification unit sends a reminder when a task deadline is approaching. As a result, the business support system according to this embodiment can streamline the user's work and reduce stress.

[0030] The reception desk accepts voice input. For example, the reception desk can accept voice input using a microphone. Specifically, voice data is collected when the user speaks into the microphone. This voice data is processed in real time and transmitted to the system. The reception desk can also accept voice input using specific speech recognition technologies. For example, the reception desk can convert voice input to text using a speech recognition algorithm. The speech recognition algorithm analyzes the voice data and converts speech to text by identifying phonemes and words. This process includes voice feature extraction, application of acoustic models, and application of language models. Deep learning models are often used in speech recognition technology, enabling highly accurate speech recognition. Furthermore, the reception desk can use noise cancellation technology to remove ambient noise and improve the accuracy of voice input. This allows users to accurately input voice information even in noisy environments. In addition to voice input, the reception desk can provide additional input methods to understand the user's intent. For example, users can use gestures or touch operations in conjunction with voice input for more intuitive operation. This allows the reception desk to accommodate diverse user input methods and provide a flexible interface.

[0031] The analysis unit converts voice instructions into text and identifies appropriate actions. For example, the analysis unit can convert voice instructions into text using a speech recognition algorithm. A speech recognition algorithm analyzes voice data and identifies phonemes and words to convert speech into text. This process includes voice feature extraction, application of acoustic models, and application of language models. Furthermore, the analysis unit can also analyze voice instructions using natural language processing techniques to identify appropriate actions. Natural language processing techniques include morphological analysis, syntactic analysis, and semantic analysis, enabling an accurate understanding of the content of voice instructions. For example, the analysis unit can analyze the content of voice instructions and identify actions such as scheduling meetings or assigning tasks. The analysis unit can use contextual analysis and intent recognition techniques to understand the context and intent of voice instructions. This allows for the identification of appropriate actions even when user instructions are ambiguous. Furthermore, the analysis unit can perform more accurate analysis by referring to past data and user history. For example, it can identify appropriate actions for user instructions based on past meeting schedules and task history. This allows the analysis unit to accurately understand the user's intent and quickly identify the appropriate action.

[0032] The scheduling unit schedules meetings based on instructions analyzed by the analysis unit. For example, the scheduling unit can check participants' schedules and suggest the optimal time. Specifically, it retrieves each participant's calendar information and checks their availability. This allows it to suggest the best meeting time that everyone can attend. The scheduling unit can also integrate with calendar applications to automatically set schedules. For example, it checks participants' availability and suggests the optimal meeting time. Furthermore, the scheduling unit can reserve meeting locations and resources. For example, it checks meeting room availability and reserves a suitable room. For online meetings, it can also integrate with video conferencing systems and automatically generate meeting links. This allows the scheduling unit to efficiently schedule meetings, significantly reducing user effort. Additionally, the scheduling unit can set meeting reminders and send notifications to participants. This ensures participants don't forget about the meeting and can participate smoothly.

[0033] The task assignment unit assigns tasks based on instructions analyzed by the analysis unit. For example, the task assignment unit can consider each member's workload and assign tasks to the appropriate member. Specifically, it evaluates each member's current task status and workload and assigns new tasks to the most suitable member. The task assignment unit can also consider members' skill sets and assign tasks to the most suitable members. For example, it can prioritize assigning tasks requiring specific skills to members with those skills, enabling efficient task execution. Furthermore, the task assignment unit can set task priorities and assign important tasks preferentially, ensuring that important tasks are processed quickly. The task assignment unit can also monitor task progress and reassign tasks as needed. For example, if a particular member is overloaded, tasks can be reassigned to other members to balance the workload. This allows the task assignment unit to achieve efficient and flexible task management, improving the overall team productivity.

[0034] The automation unit automates the management of schedules and task progress set by the scheduling unit and task assignment unit. For example, the automation unit can automate the setting of regular schedules and the management of task progress. Specifically, the automation unit can automate the scheduling of regular meetings, preventing double bookings and missed appointments. The automation unit can also prioritize tasks and send reminders and notifications for important tasks. For example, it can send reminders when task deadlines are approaching to alert users. Furthermore, the automation unit can monitor task progress in real time and reassign tasks as needed. This prevents delays and incomplete tasks. The automation unit can also automatically take appropriate actions depending on the progress of tasks. For example, it can automatically start the next task when a particular task is completed. It can also send notifications to supervisors and relevant parties depending on the progress of tasks. As a result, the automation unit can significantly improve the efficiency of task management and reduce the workload of users.

[0035] The notification unit sends reminders and notifications for schedules and tasks managed by the automation unit. For example, the notification unit can send reminders for important tasks. Specifically, it can send reminders to users as task deadlines approach to encourage task completion. The notification unit can also send notifications according to the progress of tasks. For example, it can send notifications to supervisors and relevant parties when a task is completed. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the notification unit to provide users with information quickly and reliably, and to smoothly advance task progress. In addition, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of notification content. For example, by adjusting the timing and content of reminders according to user requests, it can achieve more effective notifications. This allows the notification unit to streamline users' work and reduce stress.

[0036] The reception unit can accept voice input. The reception unit can accept voice input using, for example, a microphone. The reception unit can also accept voice input using specific speech recognition technology. For example, the reception unit can convert voice input to text using a speech recognition algorithm. This allows users to easily give instructions by accepting voice input. Voice input includes, for example, using a microphone or using specific speech recognition technology. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, after receiving voice input, the reception unit can input the voice data into a generating AI and have the generating AI perform the generation of text data from the voice data.

[0037] The analysis unit can convert voice instructions into text and identify appropriate actions. For example, the analysis unit can convert voice instructions into text using a speech recognition algorithm. The analysis unit can also analyze voice instructions using natural language processing techniques to identify appropriate actions. For example, the analysis unit can analyze the content of voice instructions and identify actions such as scheduling meetings or assigning tasks. This ensures smooth execution of instructions by converting voice instructions into text and identifying appropriate actions. Voice instructions include, for example, the type of instruction and the format of the voice command. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, after receiving a voice instruction, the analysis unit can input the voice data into a generating AI and have the generating AI generate text data from the voice data.

[0038] The scheduling unit can check participants' schedules and suggest appropriate times. For example, the scheduling unit checks participants' schedules and suggests the optimal time. The scheduling unit can also integrate with calendar applications to automatically set schedules. For example, the scheduling unit checks participants' availability and suggests the optimal meeting time. This streamlines meeting scheduling by checking participants' schedules and suggesting the optimal time. Participant schedules include, for example, integration with calendar applications and methods for obtaining schedules. Some or all of the above-described processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input participant schedule data into a generating AI and have the generating AI suggest the optimal meeting time.

[0039] The task assignment unit can assign tasks to appropriate members based on each member's workload. For example, the task assignment unit considers each member's workload and assigns tasks to the appropriate member. The task assignment unit can also consider members' skill sets and assign tasks to the most suitable member. For example, the task assignment unit evaluates each member's workload and assigns appropriate tasks. This enables efficient task assignment by considering each member's workload and assigning tasks to the appropriate member. Workload includes, for example, the amount and difficulty of the tasks. Some or all of the above-described processes in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input member workload data into a generating AI and have the generating AI perform the optimal task assignment.

[0040] The automation unit can automate regular scheduling and task progress management. For example, the automation unit can automate regular scheduling, saving users time. The automation unit can also automate task progress management, preventing tasks from being missed or delayed. For example, the automation unit can automate scheduling of regular meetings, preventing double bookings and forgotten appointments. The automation unit can also prioritize tasks and send reminders and notifications for important tasks. This reduces user effort by automating regular scheduling and task progress management. Some or all of the above processes in the automation unit may be performed using AI, for example, or not. For example, the automation unit can input schedule data and task data into a generating AI and have the generating AI perform the automation of scheduling and task management.

[0041] The notification unit can send reminders and notifications for important tasks. For example, the notification unit can send reminders for important tasks. The notification unit can also send notifications according to the progress of tasks. For example, the notification unit can send a reminder when a task deadline is approaching. By sending reminders and notifications for important tasks, it is possible to prevent tasks from being missed or delayed. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input task data into a generating AI and have the generating AI execute the sending of reminders and notifications.

[0042] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit prioritizes receiving voice commands that the user has frequently used in the past. The reception unit can also predict commands to be used during specific time periods based on the user's past voice input history and receive them accordingly. For example, the reception unit analyzes the user's past voice input history and proposes the most efficient reception method. This allows the optimal reception method to be selected by analyzing the user's past voice input history. The voice input history includes, for example, past voice commands and how the history is saved. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's voice input history data into a generating AI and have the generating AI select the optimal reception method.

[0043] The reception unit can filter voice input based on the user's current work status and areas of interest. For example, the reception unit can prioritize voice input related to a project the user is currently working on. The reception unit can also filter and receive relevant voice input based on the user's areas of interest. For example, the reception unit can consider the user's current work status and prioritize voice input of high importance. This allows the system to prioritize receiving highly relevant instructions by filtering voice input based on the user's current work status and areas of interest. Work status includes, for example, the progress of current tasks and the priority of tasks. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's work status data and areas of interest data into a generating AI and have the generating AI perform the filtering of voice input.

[0044] The reception unit can prioritize receiving voice input by considering the user's geographical location information. For example, if the user is in a specific location, the reception unit will prioritize receiving voice input related to that location. The reception unit can also prioritize receiving voice input related to locations close to the user's current location. For example, the reception unit can filter and receive voice input based on the user's geographical location information to ensure it receives relevant input. Geographical location information includes, for example, methods for acquiring GPS data and methods for filtering based on location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI perform the filtering of voice input.

[0045] The reception unit can analyze the user's social media activity when receiving voice input and accept relevant input. For example, the reception unit can prioritize receiving voice input related to the user's current interests from their social media activity. The reception unit can also analyze the user's social media activity and filter the relevant voice input before accepting it. For example, the reception unit can prioritize receiving voice input of high importance based on the user's social media activity. This allows the reception unit to prioritize receiving relevant voice input by analyzing the user's social media activity. Social media activity includes, for example, methods for analyzing post content and criteria for evaluating activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of voice input.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice instructions. For example, the analysis unit can perform a detailed analysis for instructions of high importance, and a simplified analysis for instructions of low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the instructions. This allows for detailed analysis of important instructions by adjusting the level of detail based on the importance of the instructions. The importance of an instruction includes, for example, the priority of the task or the urgency of the instruction. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input instruction importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of the instruction when analyzing voice instructions. For example, the analysis unit can apply a schedule analysis algorithm to instructions regarding scheduling meetings. The analysis unit can also apply a task analysis algorithm to instructions regarding task assignment. For other instructions, the analysis unit can apply an analysis algorithm appropriate to the category. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the instruction. The categories of instructions include, for example, business categories and task categories. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input instruction category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0048] The analysis unit can determine the priority of analysis based on the timing of instruction submission when analyzing voice instructions. For example, the analysis unit will prioritize the analysis of urgent instructions. The analysis unit can also analyze normal instructions with normal priority. For example, the analysis unit will postpone the analysis of older instructions. This allows for the priority of analysis of urgent instructions by determining the priority of analysis based on the timing of instruction submission. The timing of instruction submission includes, for example, criteria for determining priority based on submission timing. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input instruction submission timing data into a generating AI and have the generating AI perform the determination of analysis priority.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the instructions when analyzing voice instructions. For example, the analysis unit can prioritize the analysis of instructions that are highly relevant. The analysis unit can also postpone the analysis of instructions that are less relevant. For example, the analysis unit determines the order of analysis according to the relevance of the instructions. This allows for the prioritization of highly relevant instructions by adjusting the order of analysis based on the relevance of the instructions. The relevance of the instructions includes, for example, criteria for adjusting the order of analysis based on relevance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input instruction relevance data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0050] The scheduling unit can suggest the optimal time when scheduling by referring to the participant's past scheduling history. For example, the scheduling unit can suggest the most convenient time from the participant's past scheduling history. The scheduling unit can also analyze the participant's past scheduling history and suggest times that avoid overlap. For example, the scheduling unit suggests the optimal meeting time based on the participant's past scheduling history. In this way, the optimal meeting time can be suggested by referring to the participant's past scheduling history. The scheduling history includes, for example, past meeting history and how the history is saved. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the participant's scheduling history data into a generating AI and have the generating AI suggest the optimal meeting time.

[0051] The scheduling unit can customize the schedule based on the participants' current work status when setting the schedule. For example, the scheduling unit considers the participants' current workload and proposes the optimal meeting time. The scheduling unit can also adjust the schedule based on the participants' current work status. For example, the scheduling unit analyzes the participants' current work status and proposes the most efficient schedule. This allows for efficient scheduling by customizing the schedule based on the participants' current work status. Work status includes, for example, the progress of current tasks and the priority of tasks. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input participants' work status data into a generating AI and have the generating AI perform the schedule customization.

[0052] The scheduling unit can suggest the optimal time when scheduling, taking into account the geographical location information of the participants. For example, if the participants are in different locations, the scheduling unit will suggest the optimal meeting time. The scheduling unit can also suggest a schedule that takes travel time into account based on the geographical location information of the participants. For example, the scheduling unit will analyze the geographical location information of the participants and suggest the most efficient meeting time. In this way, the optimal meeting time can be suggested by taking into account the geographical location information of the participants. Geographical location information includes, for example, methods for acquiring GPS data and methods for filtering based on location information. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without using AI. For example, the scheduling unit can input the geographical location information data of the participants into a generating AI and have the generating AI perform the task of suggesting the optimal meeting time.

[0053] The scheduling unit can analyze participants' social media activity and propose a schedule when setting a schedule. For example, the scheduling unit can propose the optimal meeting time based on participants' social media activity. The scheduling unit can also analyze participants' social media activity and propose a relevant schedule. For example, the scheduling unit can propose the most efficient schedule based on participants' social media activity. In this way, by analyzing participants' social media activity, the optimal meeting time can be proposed. Social media activity includes, for example, methods for analyzing post content and criteria for evaluating activity. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input participants' social media activity data into a generating AI and have the generating AI execute schedule proposals.

[0054] The task assignment unit can assign tasks to the most suitable member by referring to each member's past work history when assigning tasks. For example, the task assignment unit assigns tasks to the most suitable member based on each member's past work history. The task assignment unit can also analyze each member's past work history and assign tasks in a way that avoids duplication. For example, the task assignment unit performs optimal task assignment based on each member's past work history. This allows the task to be assigned to the most suitable member by referring to each member's past work history. Work history includes, for example, past task history and how the history is saved. Some or all of the above processing in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input member work history data into a generating AI and have the generating AI perform optimal task assignment.

[0055] The task assignment unit can customize tasks based on each member's current workload when assigning tasks. For example, the task assignment unit considers each member's current workload and assigns the most suitable task. The task assignment unit can also adjust tasks based on each member's current work situation. For example, the task assignment unit analyzes each member's current workload and assigns tasks in the most efficient way. This allows for efficient task assignment by customizing tasks based on each member's current workload. Workload includes, for example, the amount and difficulty of the current tasks. Some or all of the above processing in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input member workload data into a generating AI and have the generating AI perform task customization.

[0056] The task assignment unit can assign tasks to the most suitable members by considering each member's geographical location information during task assignment. For example, the task assignment unit can assign tasks to the most suitable members based on each member's geographical location information. The task assignment unit can also assign tasks in a way that minimizes travel time by considering each member's geographical location information. For example, the task assignment unit analyzes each member's geographical location information and performs the most efficient task assignment. This allows the task assignment to be assigned to the most suitable member by considering each member's geographical location information. Geographical location information includes, for example, methods for acquiring GPS data and methods for filtering based on location information. Some or all of the above processing in the task assignment unit may be performed using, for example, AI, or without AI. For example, the task assignment unit can input the geographical location information data of members into a generating AI and have the generating AI perform task assignment optimization.

[0057] The task assignment unit can analyze each member's social media activity and assign tasks accordingly. For example, the task assignment unit can assign the most suitable task based on each member's social media activity. The task assignment unit can also analyze each member's social media activity and assign relevant tasks. For example, the task assignment unit can perform the most efficient task assignment based on each member's social media activity. This allows for optimal task assignment by analyzing each member's social media activity. Social media activity includes, for example, methods for analyzing posted content and criteria for evaluating activities. Some or all of the above processing in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input member social media activity data into a generating AI and have the generating AI perform task assignment optimization.

[0058] The automation unit can select the optimal automation method by referring to past automation history during automation. For example, the automation unit can select the most effective automation method from past automation history. The automation unit can also analyze past automation history and perform automation in a way that avoids duplication. For example, the automation unit can propose the optimal automation method based on past automation history. This allows the optimal automation method to be selected by referring to past automation history. Automation history includes, for example, past automation tasks and how the history is saved. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input past automation history data into a generating AI and have the generating AI perform the selection of an automation method.

[0059] The automation unit can customize the automation methods based on the current work situation during automation. For example, the automation unit considers the current work situation and provides the optimal automation method. The automation unit can also adjust the automation methods based on the current work situation. For example, the automation unit analyzes the current work situation and proposes the most efficient automation method. This allows for efficient automation by customizing the automation methods based on the current work situation. The work situation includes, for example, the progress of current tasks and the priority of tasks. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input current work situation data into a generating AI and have the generating AI perform the customization of the automation methods.

[0060] The automation unit can select the optimal automation method by considering geographical location information during automation. For example, the automation unit selects the optimal automation method based on geographical location information. The automation unit can also perform automation in a way that minimizes travel time by considering geographical location information. For example, the automation unit analyzes geographical location information and proposes the most efficient automation method. This allows for the selection of the optimal automation method by considering geographical location information. Geographical location information includes, for example, methods for acquiring GPS data and filtering methods based on location information. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input geographical location data into a generating AI and have the generating AI perform the selection of an automation method.

[0061] The automation unit can analyze social media activity and propose automation methods during the automation process. For example, the automation unit can propose the optimal automation method based on social media activity. The automation unit can also analyze social media activity and provide relevant automation methods. For example, the automation unit proposes the most efficient automation method based on social media activity. In this way, the optimal automation method can be proposed by analyzing social media activity. Social media activity includes, for example, methods for analyzing post content and evaluation criteria for activity. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input social media activity data into a generating AI and have the generating AI execute the proposal of automation methods.

[0062] The notification unit can select the optimal notification method by referring to past notification history when issuing a notification. For example, the notification unit can select the most effective notification method from past notification history. The notification unit can also analyze past notification history and send notifications in a way that avoids duplication. For example, the notification unit can propose the optimal notification method based on past notification history. This allows the optimal notification method to be selected by referring to past notification history. The notification history includes, for example, past notification tasks and how the history is saved. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI perform the selection of the notification method.

[0063] The notification unit can customize the notification method based on the current work status when a notification is sent. For example, the notification unit considers the current work status and provides the optimal notification method. The notification unit can also adjust the notification method based on the current work status. For example, the notification unit analyzes the current work status and proposes the most efficient notification method. This allows for efficient notifications by customizing the notification method based on the current work status. The work status includes, for example, the progress of the current task and the priority of the task. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input current work status data into a generating AI and have the generating AI perform the customization of the notification method.

[0064] The notification unit can select the optimal notification method when sending a notification, taking geographical location information into consideration. For example, the notification unit can select the optimal notification method based on geographical location information. The notification unit can also send notifications in a way that minimizes travel time, taking geographical location information into consideration. For example, the notification unit can analyze geographical location information and propose the most efficient notification method. This allows for the selection of the optimal notification method by considering geographical location information. Geographical location information includes, for example, methods for acquiring GPS data and methods for filtering based on location information. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input geographical location data into a generating AI and have the generating AI perform the selection of the notification method.

[0065] The notification unit can analyze social media activity and propose notification methods when issuing notifications. For example, the notification unit can propose the most suitable notification method based on social media activity. The notification unit can also analyze social media activity and provide relevant notification methods. For example, the notification unit can propose the most efficient notification method based on social media activity. In this way, the optimal notification method can be proposed by analyzing social media activity. Social media activity includes, for example, methods for analyzing posted content and criteria for evaluating activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input social media activity data into a generating AI and have the generating AI execute the proposal of notification methods.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] Business support systems can analyze users' past work history and assign tasks optimally. For example, they can assign similar tasks again to members who have successfully completed similar tasks in the past. They can also identify areas of expertise for specific members based on past work history and prioritize assigning tasks related to those areas. Furthermore, they can evenly distribute the workload among members based on past work history. In short, they can leverage past work history to achieve optimal task assignments.

[0068] Business support systems can assign tasks while considering the user's geographical location. For example, if a user is in a specific location, tasks related to that location can be assigned preferentially. Tasks related to locations close to the user's current location can also be assigned preferentially. Furthermore, tasks can be assigned in a way that minimizes travel time based on the user's geographical location. This allows for optimal task assignment by considering geographical location.

[0069] The business support system can analyze users' social media activity and assign relevant tasks. For example, it can prioritize tasks related to a user's current interests based on their social media activity. It can also analyze a user's social media activity and filter and assign relevant tasks. Furthermore, it can perform the most efficient task assignment based on the user's social media activity. In this way, optimal task assignment can be achieved by analyzing social media activity.

[0070] The business support system can suggest the optimal meeting time by referring to the user's past schedule history. For example, it can suggest the most convenient time based on past schedule history. It can also analyze past schedule history and suggest times that avoid overlaps. Furthermore, it can suggest the optimal meeting time based on past schedule history. In this way, the system can suggest the optimal meeting time by referring to past schedule history.

[0071] The business support system can customize schedules based on the user's current work situation. For example, it can suggest optimal meeting times considering the current workload. It can also adjust schedules based on the current work situation. Furthermore, it can analyze the current work situation and suggest the most efficient schedule. This allows for efficient scheduling by customizing schedules based on the current work situation.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The reception desk accepts voice input. For example, it can accept voice input using a microphone, or it can convert voice input to text using specific speech recognition technology. Step 2: The analysis unit analyzes the voice instructions received by the reception unit. The voice instructions are converted into text, and natural language processing technology is used to identify the appropriate action. Step 3: The scheduling unit schedules the meeting based on the instructions analyzed by the analysis unit. It checks the participants' schedules, suggests the optimal time, and integrates with the calendar application. Step 4: The task assignment unit assigns tasks based on the instructions analyzed by the analysis unit. They consider each member's workload and skill set and assign tasks to the appropriate members. Step 5: The automation unit automates the progress management of schedules and tasks set by the scheduling unit and task assignment unit. It automates regular schedule setting and task progress management, and prioritizes tasks. Step 6: The notification unit sends reminders and notifications for schedules and tasks managed by the automation unit. It sends reminders for important tasks and notifications based on the progress of those tasks.

[0074] (Example of form 2) The business support system according to an embodiment of the present invention is an AI-based business support platform developed for business owners and team managers of small and medium-sized enterprises. This business support system maximizes productivity by simplifying business processes and automating tasks. The business support system is equipped with a voice input function, making it easy to set and change meeting schedules and assign tasks. The business support system supports improved work efficiency and reduced stress, enabling smoother business operations. First, the user gives instructions to set and change meeting schedules and assign tasks via voice input. For example, the user gives a voice command such as, "Schedule a meeting for 10 AM tomorrow." This voice command is input into the business support system. Next, the business support system analyzes the input voice command and takes appropriate action. For example, when setting a meeting schedule, the business support system checks the participants' schedules and suggests the optimal time. Also, when assigning tasks, the business support system considers the workload of each member and assigns tasks to the appropriate members. Furthermore, the business support system automates business processes. For example, it reduces the user's workload by automating the setting of regular meeting schedules and the management of task progress. Furthermore, it prioritizes tasks and sends reminders and notifications for important tasks. This system allows users to improve work efficiency. For example, automating meeting scheduling prevents double bookings and missed appointments. Automated task progress management prevents tasks from being overlooked or delayed. Business support systems help improve work efficiency and reduce stress. Users can simplify cumbersome tasks, saving time and energy. For example, scheduling meetings and assigning tasks can be easily done using voice input, reducing the workload. Also, automating business processes allows users to focus on important tasks. In this way, business support systems are a powerful tool for small and medium-sized business owners and team managers to improve work efficiency and reduce stress.This allows business support systems to streamline users' work and reduce stress.

[0075] The business support system according to this embodiment comprises a reception unit, an analysis unit, a schedule setting unit, a task assignment unit, an automation unit, and a notification unit. The reception unit receives voice input. The reception unit can receive voice input using, for example, a microphone. The reception unit can also receive voice input using specific speech recognition technology. For example, the reception unit can convert voice input to text using a speech recognition algorithm. The analysis unit converts voice instructions to text and identifies appropriate actions. The analysis unit can convert voice instructions to text using, for example, a speech recognition algorithm. The analysis unit can also analyze voice instructions using natural language processing technology and identify appropriate actions. For example, the analysis unit analyzes the content of voice instructions and identifies actions such as scheduling meetings or assigning tasks. The schedule setting unit schedules meetings based on the instructions analyzed by the analysis unit. The schedule setting unit can, for example, check participants' schedules and suggest the optimal time. The schedule setting unit can also link with a calendar application to automatically set schedules. For example, the scheduling unit checks participants' availability and proposes the optimal meeting time. The task assignment unit assigns tasks based on instructions analyzed by the analysis unit. The task assignment unit can, for example, consider each member's workload and assign tasks to the appropriate member. It can also consider members' skill sets and assign tasks to the most suitable member. For example, the task assignment unit evaluates members' workloads and assigns appropriate tasks. The automation unit automates the progress management of schedules and tasks set by the scheduling and task assignment units. The automation unit can, for example, automate the setting of regular schedules and the progress management of tasks. It can also prioritize tasks and send reminders and notifications for important tasks. For example, the automation unit can automate the scheduling of regular meetings to prevent double bookings and missed appointments.The notification unit sends reminders and notifications for schedules and tasks managed by the automation unit. For example, the notification unit can send reminders for important tasks. It can also send notifications according to the progress of tasks. For example, the notification unit sends a reminder when a task deadline is approaching. As a result, the business support system according to this embodiment can streamline the user's work and reduce stress.

[0076] The reception desk accepts voice input. For example, the reception desk can accept voice input using a microphone. Specifically, voice data is collected when the user speaks into the microphone. This voice data is processed in real time and transmitted to the system. The reception desk can also accept voice input using specific speech recognition technologies. For example, the reception desk can convert voice input to text using a speech recognition algorithm. The speech recognition algorithm analyzes the voice data and converts speech to text by identifying phonemes and words. This process includes voice feature extraction, application of acoustic models, and application of language models. Deep learning models are often used in speech recognition technology, enabling highly accurate speech recognition. Furthermore, the reception desk can use noise cancellation technology to remove ambient noise and improve the accuracy of voice input. This allows users to accurately input voice information even in noisy environments. In addition to voice input, the reception desk can provide additional input methods to understand the user's intent. For example, users can use gestures or touch operations in conjunction with voice input for more intuitive operation. This allows the reception desk to accommodate diverse user input methods and provide a flexible interface.

[0077] The analysis unit converts voice instructions into text and identifies appropriate actions. For example, the analysis unit can convert voice instructions into text using a speech recognition algorithm. A speech recognition algorithm analyzes voice data and identifies phonemes and words to convert speech into text. This process includes voice feature extraction, application of acoustic models, and application of language models. Furthermore, the analysis unit can also analyze voice instructions using natural language processing techniques to identify appropriate actions. Natural language processing techniques include morphological analysis, syntactic analysis, and semantic analysis, enabling an accurate understanding of the content of voice instructions. For example, the analysis unit can analyze the content of voice instructions and identify actions such as scheduling meetings or assigning tasks. The analysis unit can use contextual analysis and intent recognition techniques to understand the context and intent of voice instructions. This allows for the identification of appropriate actions even when user instructions are ambiguous. Furthermore, the analysis unit can perform more accurate analysis by referring to past data and user history. For example, it can identify appropriate actions for user instructions based on past meeting schedules and task history. This allows the analysis unit to accurately understand the user's intent and quickly identify the appropriate action.

[0078] The scheduling unit schedules meetings based on instructions analyzed by the analysis unit. For example, the scheduling unit can check participants' schedules and suggest the optimal time. Specifically, it retrieves each participant's calendar information and checks their availability. This allows it to suggest the best meeting time that everyone can attend. The scheduling unit can also integrate with calendar applications to automatically set schedules. For example, it checks participants' availability and suggests the optimal meeting time. Furthermore, the scheduling unit can reserve meeting locations and resources. For example, it checks meeting room availability and reserves a suitable room. For online meetings, it can also integrate with video conferencing systems and automatically generate meeting links. This allows the scheduling unit to efficiently schedule meetings, significantly reducing user effort. Additionally, the scheduling unit can set meeting reminders and send notifications to participants. This ensures participants don't forget about the meeting and can participate smoothly.

[0079] The task assignment unit assigns tasks based on instructions analyzed by the analysis unit. For example, the task assignment unit can consider each member's workload and assign tasks to the appropriate member. Specifically, it evaluates each member's current task status and workload and assigns new tasks to the most suitable member. The task assignment unit can also consider members' skill sets and assign tasks to the most suitable members. For example, it can prioritize assigning tasks requiring specific skills to members with those skills, enabling efficient task execution. Furthermore, the task assignment unit can set task priorities and assign important tasks preferentially, ensuring that important tasks are processed quickly. The task assignment unit can also monitor task progress and reassign tasks as needed. For example, if a particular member is overloaded, tasks can be reassigned to other members to balance the workload. This allows the task assignment unit to achieve efficient and flexible task management, improving the overall team productivity.

[0080] The automation unit automates the management of schedules and task progress set by the scheduling unit and task assignment unit. For example, the automation unit can automate the setting of regular schedules and the management of task progress. Specifically, the automation unit can automate the scheduling of regular meetings, preventing double bookings and missed appointments. The automation unit can also prioritize tasks and send reminders and notifications for important tasks. For example, it can send reminders when task deadlines are approaching to alert users. Furthermore, the automation unit can monitor task progress in real time and reassign tasks as needed. This prevents delays and incomplete tasks. The automation unit can also automatically take appropriate actions depending on the progress of tasks. For example, it can automatically start the next task when a particular task is completed. It can also send notifications to supervisors and relevant parties depending on the progress of tasks. As a result, the automation unit can significantly improve the efficiency of task management and reduce the workload of users.

[0081] The notification unit sends reminders and notifications for schedules and tasks managed by the automation unit. For example, the notification unit can send reminders for important tasks. Specifically, it can send reminders to users as task deadlines approach to encourage task completion. The notification unit can also send notifications according to the progress of tasks. For example, it can send notifications to supervisors and relevant parties when a task is completed. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the notification unit to provide users with information quickly and reliably, and to smoothly advance task progress. In addition, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of notification content. For example, by adjusting the timing and content of reminders according to user requests, it can achieve more effective notifications. This allows the notification unit to streamline users' work and reduce stress.

[0082] The reception unit can accept voice input. The reception unit can accept voice input using, for example, a microphone. The reception unit can also accept voice input using specific speech recognition technology. For example, the reception unit can convert voice input to text using a speech recognition algorithm. This allows users to easily give instructions by accepting voice input. Voice input includes, for example, using a microphone or using specific speech recognition technology. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, after receiving voice input, the reception unit can input the voice data into a generating AI and have the generating AI perform the generation of text data from the voice data.

[0083] The analysis unit can convert voice instructions into text and identify appropriate actions. For example, the analysis unit can convert voice instructions into text using a speech recognition algorithm. The analysis unit can also analyze voice instructions using natural language processing techniques to identify appropriate actions. For example, the analysis unit can analyze the content of voice instructions and identify actions such as scheduling meetings or assigning tasks. This ensures smooth execution of instructions by converting voice instructions into text and identifying appropriate actions. Voice instructions include, for example, the type of instruction and the format of the voice command. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, after receiving a voice instruction, the analysis unit can input the voice data into a generating AI and have the generating AI generate text data from the voice data.

[0084] The scheduling unit can check participants' schedules and suggest appropriate times. For example, the scheduling unit checks participants' schedules and suggests the optimal time. The scheduling unit can also integrate with calendar applications to automatically set schedules. For example, the scheduling unit checks participants' availability and suggests the optimal meeting time. This streamlines meeting scheduling by checking participants' schedules and suggesting the optimal time. Participant schedules include, for example, integration with calendar applications and methods for obtaining schedules. Some or all of the above-described processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input participant schedule data into a generating AI and have the generating AI suggest the optimal meeting time.

[0085] The task assignment unit can assign tasks to appropriate members based on each member's workload. For example, the task assignment unit considers each member's workload and assigns tasks to the appropriate member. The task assignment unit can also consider members' skill sets and assign tasks to the most suitable member. For example, the task assignment unit evaluates each member's workload and assigns appropriate tasks. This enables efficient task assignment by considering each member's workload and assigning tasks to the appropriate member. Workload includes, for example, the amount and difficulty of the tasks. Some or all of the above-described processes in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input member workload data into a generating AI and have the generating AI perform the optimal task assignment.

[0086] The automation unit can automate regular scheduling and task progress management. For example, the automation unit can automate regular scheduling, saving users time. The automation unit can also automate task progress management, preventing tasks from being missed or delayed. For example, the automation unit can automate scheduling of regular meetings, preventing double bookings and forgotten appointments. The automation unit can also prioritize tasks and send reminders and notifications for important tasks. This reduces user effort by automating regular scheduling and task progress management. Some or all of the above processes in the automation unit may be performed using AI, for example, or not. For example, the automation unit can input schedule data and task data into a generating AI and have the generating AI perform the automation of scheduling and task management.

[0087] The notification unit can send reminders and notifications for important tasks. For example, the notification unit can send reminders for important tasks. The notification unit can also send notifications according to the progress of tasks. For example, the notification unit can send a reminder when a task deadline is approaching. By sending reminders and notifications for important tasks, it is possible to prevent tasks from being missed or delayed. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input task data into a generating AI and have the generating AI execute the sending of reminders and notifications.

[0088] The reception unit can estimate the user's emotions and adjust the timing of voice input reception based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of voice input reception and wait until the user is relaxed. If the user is relaxed, the reception unit can also speed up the timing of voice input reception to smoothly receive instructions. For example, if the user is in a hurry, the reception unit can make the timing of voice input reception immediate to quickly receive instructions. By adjusting the timing of voice input reception according to the user's emotions, instructions can be received at a more appropriate time. 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 reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the timing of voice input reception.

[0089] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit prioritizes receiving voice commands that the user has frequently used in the past. The reception unit can also predict commands to be used during specific time periods based on the user's past voice input history and receive them accordingly. For example, the reception unit analyzes the user's past voice input history and proposes the most efficient reception method. This allows the optimal reception method to be selected by analyzing the user's past voice input history. The voice input history includes, for example, past voice commands and how the history is saved. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's voice input history data into a generating AI and have the generating AI select the optimal reception method.

[0090] The reception unit can filter voice input based on the user's current work status and areas of interest. For example, the reception unit can prioritize voice input related to a project the user is currently working on. The reception unit can also filter and receive relevant voice input based on the user's areas of interest. For example, the reception unit can consider the user's current work status and prioritize voice input of high importance. This allows the system to prioritize receiving highly relevant instructions by filtering voice input based on the user's current work status and areas of interest. Work status includes, for example, the progress of current tasks and the priority of tasks. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's work status data and areas of interest data into a generating AI and have the generating AI perform the filtering of voice input.

[0091] The reception unit can estimate the user's emotions and determine the priority of voice input to be received based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize receiving high-priority voice input. If the user is relaxed, the reception unit can also receive voice input with normal priority. For example, if the user is in a hurry, the reception unit will prioritize receiving urgent voice input. This allows important instructions to be received preferentially by determining the priority of voice input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform the determination of voice input priorities.

[0092] The reception unit can prioritize receiving voice input by considering the user's geographical location information. For example, if the user is in a specific location, the reception unit will prioritize receiving voice input related to that location. The reception unit can also prioritize receiving voice input related to locations close to the user's current location. For example, the reception unit can filter and receive voice input based on the user's geographical location information to ensure it receives relevant input. Geographical location information includes, for example, methods for acquiring GPS data and methods for filtering based on location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI perform the filtering of voice input.

[0093] The reception unit can analyze the user's social media activity when receiving voice input and accept relevant input. For example, the reception unit can prioritize receiving voice input related to the user's current interests from their social media activity. The reception unit can also analyze the user's social media activity and filter the relevant voice input before accepting it. For example, the reception unit can prioritize receiving voice input of high importance based on the user's social media activity. This allows the reception unit to prioritize receiving relevant voice input by analyzing the user's social media activity. Social media activity includes, for example, methods for analyzing post content and criteria for evaluating activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of voice input.

[0094] The analysis unit can estimate the user's emotions and adjust the analysis method of voice instructions based on the estimated user emotions. For example, if the user is stressed, the analysis unit can apply a concise and clear analysis method. If the user is relaxed, the analysis unit can also apply a detailed analysis method. For example, if the user is in a hurry, the analysis unit can apply a method that performs analysis quickly. This allows for more appropriate analysis by adjusting the analysis method of voice instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis method of voice instructions.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice instructions. For example, the analysis unit can perform a detailed analysis for instructions of high importance, and a simplified analysis for instructions of low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the instructions. This allows for detailed analysis of important instructions by adjusting the level of detail based on the importance of the instructions. The importance of an instruction includes, for example, the priority of the task or the urgency of the instruction. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input instruction importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0096] The analysis unit can apply different analysis algorithms depending on the category of the instruction when analyzing voice instructions. For example, the analysis unit can apply a schedule analysis algorithm to instructions regarding scheduling meetings. The analysis unit can also apply a task analysis algorithm to instructions regarding task assignment. For other instructions, the analysis unit can apply an analysis algorithm appropriate to the category. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the instruction. The categories of instructions include, for example, business categories and task categories. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input instruction category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0098] The analysis unit can determine the priority of analysis based on the timing of instruction submission when analyzing voice instructions. For example, the analysis unit will prioritize the analysis of urgent instructions. The analysis unit can also analyze normal instructions with normal priority. For example, the analysis unit will postpone the analysis of older instructions. This allows for the priority of analysis of urgent instructions by determining the priority of analysis based on the timing of instruction submission. The timing of instruction submission includes, for example, criteria for determining priority based on submission timing. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input instruction submission timing data into a generating AI and have the generating AI perform the determination of analysis priority.

[0099] The analysis unit can adjust the order of analysis based on the relevance of the instructions when analyzing voice instructions. For example, the analysis unit can prioritize the analysis of instructions that are highly relevant. The analysis unit can also postpone the analysis of instructions that are less relevant. For example, the analysis unit determines the order of analysis according to the relevance of the instructions. This allows for the prioritization of highly relevant instructions by adjusting the order of analysis based on the relevance of the instructions. The relevance of the instructions includes, for example, criteria for adjusting the order of analysis based on relevance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input instruction relevance data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0100] The scheduling unit can estimate the user's emotions and adjust the scheduling method based on the estimated emotions. For example, if the user is stressed, the scheduling unit can provide a simple and intuitive scheduling method. If the user is relaxed, the scheduling unit can also provide detailed scheduling options. For example, if the user is in a hurry, the scheduling unit can provide a way to quickly set a schedule. This allows for more appropriate scheduling by adjusting the scheduling method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI adjust the scheduling method.

[0101] The scheduling unit can suggest the optimal time when scheduling by referring to the participant's past scheduling history. For example, the scheduling unit can suggest the most convenient time from the participant's past scheduling history. The scheduling unit can also analyze the participant's past scheduling history and suggest times that avoid overlap. For example, the scheduling unit suggests the optimal meeting time based on the participant's past scheduling history. In this way, the optimal meeting time can be suggested by referring to the participant's past scheduling history. The scheduling history includes, for example, past meeting history and how the history is saved. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the participant's scheduling history data into a generating AI and have the generating AI suggest the optimal meeting time.

[0102] The scheduling unit can customize the schedule based on the participants' current work status when setting the schedule. For example, the scheduling unit considers the participants' current workload and proposes the optimal meeting time. The scheduling unit can also adjust the schedule based on the participants' current work status. For example, the scheduling unit analyzes the participants' current work status and proposes the most efficient schedule. This allows for efficient scheduling by customizing the schedule based on the participants' current work status. Work status includes, for example, the progress of current tasks and the priority of tasks. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input participants' work status data into a generating AI and have the generating AI perform the schedule customization.

[0103] The scheduling unit can estimate the user's emotions and determine scheduling priorities based on those emotions. For example, if the user is stressed, the scheduling unit will prioritize high-priority schedules. If the user is relaxed, the scheduling unit can also prioritize schedules according to normal priorities. For example, if the user is in a hurry, the scheduling unit will prioritize urgent schedules. This allows important schedules to be prioritized by determining scheduling priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI determine the scheduling priorities.

[0104] The scheduling unit can suggest the optimal time when scheduling, taking into account the geographical location information of the participants. For example, if the participants are in different locations, the scheduling unit will suggest the optimal meeting time. The scheduling unit can also suggest a schedule that takes travel time into account based on the geographical location information of the participants. For example, the scheduling unit will analyze the geographical location information of the participants and suggest the most efficient meeting time. In this way, the optimal meeting time can be suggested by taking into account the geographical location information of the participants. Geographical location information includes, for example, methods for acquiring GPS data and methods for filtering based on location information. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without using AI. For example, the scheduling unit can input the geographical location information data of the participants into a generating AI and have the generating AI perform the task of suggesting the optimal meeting time.

[0105] The scheduling unit can analyze participants' social media activity and propose a schedule when setting a schedule. For example, the scheduling unit can propose the optimal meeting time based on participants' social media activity. The scheduling unit can also analyze participants' social media activity and propose a relevant schedule. For example, the scheduling unit can propose the most efficient schedule based on participants' social media activity. In this way, by analyzing participants' social media activity, the optimal meeting time can be proposed. Social media activity includes, for example, methods for analyzing post content and criteria for evaluating activity. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input participants' social media activity data into a generating AI and have the generating AI execute schedule proposals.

[0106] The task assignment unit can estimate the user's emotions and adjust the task assignment method based on the estimated emotions. For example, if the user is stressed, the task assignment unit can provide a simple and intuitive task assignment method. If the user is relaxed, the task assignment unit can also provide detailed setting options. For example, if the user is in a hurry, the task assignment unit can provide a method for quickly assigning tasks. This allows for more appropriate task assignment by adjusting the task assignment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 task assignment unit may be performed using AI or not using AI. For example, the task assignment unit can input user emotion data into a generative AI and have the generative AI adjust the task assignment method.

[0107] The task assignment unit can assign tasks to the most suitable member by referring to each member's past work history when assigning tasks. For example, the task assignment unit assigns tasks to the most suitable member based on each member's past work history. The task assignment unit can also analyze each member's past work history and assign tasks in a way that avoids duplication. For example, the task assignment unit performs optimal task assignment based on each member's past work history. This allows the task to be assigned to the most suitable member by referring to each member's past work history. Work history includes, for example, past task history and how the history is saved. Some or all of the above processing in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input member work history data into a generating AI and have the generating AI perform optimal task assignment.

[0108] The task assignment unit can customize tasks based on each member's current workload when assigning tasks. For example, the task assignment unit considers each member's current workload and assigns the most suitable task. The task assignment unit can also adjust tasks based on each member's current work situation. For example, the task assignment unit analyzes each member's current workload and assigns tasks in the most efficient way. This allows for efficient task assignment by customizing tasks based on each member's current workload. Workload includes, for example, the amount and difficulty of the current tasks. Some or all of the above processing in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input member workload data into a generating AI and have the generating AI perform task customization.

[0109] The task assignment unit can estimate the user's emotions and determine the priority of task assignments based on the estimated emotions. For example, if the user is stressed, the task assignment unit will prioritize assigning high-priority tasks. If the user is relaxed, the task assignment unit can also assign tasks with normal priority. For example, if the user is in a hurry, the task assignment unit will prioritize assigning urgent tasks. This allows for the priority of important tasks to be assigned according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the task assignment unit may be performed using AI or not. For example, the task assignment unit can input user emotion data into a generative AI and have the generative AI determine the priority of task assignments.

[0110] The task assignment unit can assign tasks to the most suitable members by considering each member's geographical location information during task assignment. For example, the task assignment unit can assign tasks to the most suitable members based on each member's geographical location information. The task assignment unit can also assign tasks in a way that minimizes travel time by considering each member's geographical location information. For example, the task assignment unit analyzes each member's geographical location information and performs the most efficient task assignment. This allows the task assignment to be assigned to the most suitable member by considering each member's geographical location information. Geographical location information includes, for example, methods for acquiring GPS data and methods for filtering based on location information. Some or all of the above processing in the task assignment unit may be performed using, for example, AI, or without AI. For example, the task assignment unit can input the geographical location information data of members into a generating AI and have the generating AI perform task assignment optimization.

[0111] The task assignment unit can analyze each member's social media activity and assign tasks accordingly. For example, the task assignment unit can assign the most suitable task based on each member's social media activity. The task assignment unit can also analyze each member's social media activity and assign relevant tasks. For example, the task assignment unit can perform the most efficient task assignment based on each member's social media activity. This allows for optimal task assignment by analyzing each member's social media activity. Social media activity includes, for example, methods for analyzing posted content and criteria for evaluating activities. Some or all of the above processing in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input member social media activity data into a generating AI and have the generating AI perform task assignment optimization.

[0112] The automation unit can estimate the user's emotions and adjust the automation method based on the estimated emotions. For example, if the user is stressed, the automation unit can provide a simple and intuitive automation method. If the user is relaxed, the automation unit can also provide detailed setting options. For example, if the user is in a hurry, the automation unit can provide a method for rapid automation. This allows for more appropriate automation by adjusting the automation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 automation unit may be performed using AI or not using AI. For example, the automation unit can input user emotion data into the generative AI and have the generative AI adjust the automation method.

[0113] The automation unit can select the optimal automation method by referring to past automation history during automation. For example, the automation unit can select the most effective automation method from past automation history. The automation unit can also analyze past automation history and perform automation in a way that avoids duplication. For example, the automation unit can propose the optimal automation method based on past automation history. This allows the optimal automation method to be selected by referring to past automation history. Automation history includes, for example, past automation tasks and how the history is saved. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input past automation history data into a generating AI and have the generating AI perform the selection of an automation method.

[0114] The automation unit can customize the automation methods based on the current work situation during automation. For example, the automation unit considers the current work situation and provides the optimal automation method. The automation unit can also adjust the automation methods based on the current work situation. For example, the automation unit analyzes the current work situation and proposes the most efficient automation method. This allows for efficient automation by customizing the automation methods based on the current work situation. The work situation includes, for example, the progress of current tasks and the priority of tasks. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input current work situation data into a generating AI and have the generating AI perform the customization of the automation methods.

[0115] The automation unit can estimate the user's emotions and determine the priority of automation based on the estimated emotions. For example, if the user is stressed, the automation unit will prioritize high-priority automation. If the user is relaxed, the automation unit can also prioritize automation at the normal priority level. For example, if the user is in a hurry, the automation unit will prioritize urgent automation. This allows important automation to be prioritized by determining the priority of automation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI or not using AI. For example, the automation unit can input user emotion data into a generative AI and have the generative AI determine the priority of automation.

[0116] The automation unit can select the optimal automation method by considering geographical location information during automation. For example, the automation unit selects the optimal automation method based on geographical location information. The automation unit can also perform automation in a way that minimizes travel time by considering geographical location information. For example, the automation unit analyzes geographical location information and proposes the most efficient automation method. This allows for the selection of the optimal automation method by considering geographical location information. Geographical location information includes, for example, methods for acquiring GPS data and filtering methods based on location information. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input geographical location data into a generating AI and have the generating AI perform the selection of an automation method.

[0117] The automation unit can analyze social media activity and propose automation methods during the automation process. For example, the automation unit can propose the optimal automation method based on social media activity. The automation unit can also analyze social media activity and provide relevant automation methods. For example, the automation unit proposes the most efficient automation method based on social media activity. In this way, the optimal automation method can be proposed by analyzing social media activity. Social media activity includes, for example, methods for analyzing post content and evaluation criteria for activity. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input social media activity data into a generating AI and have the generating AI execute the proposal of automation methods.

[0118] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible notification method. If the user is relaxed, the notification unit can also provide a notification method that includes detailed information. For example, if the user is in a hurry, the notification unit can provide a concise notification method. This allows for more appropriate notifications by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0119] The notification unit can select the optimal notification method by referring to past notification history when issuing a notification. For example, the notification unit can select the most effective notification method from past notification history. The notification unit can also analyze past notification history and send notifications in a way that avoids duplication. For example, the notification unit can propose the optimal notification method based on past notification history. This allows the optimal notification method to be selected by referring to past notification history. The notification history includes, for example, past notification tasks and how the history is saved. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI perform the selection of the notification method.

[0120] The notification unit can customize the notification method based on the current work status when a notification is sent. For example, the notification unit considers the current work status and provides the optimal notification method. The notification unit can also adjust the notification method based on the current work status. For example, the notification unit analyzes the current work status and proposes the most efficient notification method. This allows for efficient notifications by customizing the notification method based on the current work status. The work status includes, for example, the progress of the current task and the priority of the task. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input current work status data into a generating AI and have the generating AI perform the customization of the notification method.

[0121] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize high-priority notifications. If the user is relaxed, the notification unit can also deliver notifications with normal priority. For example, if the user is in a hurry, the notification unit will prioritize urgent notifications. This allows important notifications to be prioritized by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0122] The notification unit can select the optimal notification method when sending a notification, taking geographical location information into consideration. For example, the notification unit can select the optimal notification method based on geographical location information. The notification unit can also send notifications in a way that minimizes travel time, taking geographical location information into consideration. For example, the notification unit can analyze geographical location information and propose the most efficient notification method. This allows for the selection of the optimal notification method by considering geographical location information. Geographical location information includes, for example, methods for acquiring GPS data and methods for filtering based on location information. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input geographical location data into a generating AI and have the generating AI perform the selection of the notification method.

[0123] The notification unit can analyze social media activity and propose notification methods when issuing notifications. For example, the notification unit can propose the most suitable notification method based on social media activity. The notification unit can also analyze social media activity and provide relevant notification methods. For example, the notification unit can propose the most efficient notification method based on social media activity. In this way, the optimal notification method can be proposed by analyzing social media activity. Social media activity includes, for example, methods for analyzing posted content and criteria for evaluating activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input social media activity data into a generating AI and have the generating AI execute the proposal of notification methods.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] Business support systems can estimate a user's emotions and adjust task assignments based on those estimates. For example, if a user is stressed, the system can change task priorities, assigning easier tasks to reduce stress. Conversely, if a user is relaxed, the system can assign more complex tasks. Furthermore, if a user is in a hurry, the system can assign urgent tasks as the top priority. This allows for increased work efficiency and reduced stress by adjusting task assignments according to the user's emotions.

[0126] Business support systems can analyze users' past work history and assign tasks optimally. For example, they can assign similar tasks again to members who have successfully completed similar tasks in the past. They can also identify areas of expertise for specific members based on past work history and prioritize assigning tasks related to those areas. Furthermore, they can evenly distribute the workload among members based on past work history. In short, they can leverage past work history to achieve optimal task assignments.

[0127] Business support systems can estimate a user's emotions and adjust notification methods based on those estimates. For example, if a user is stressed, the system can provide a simple and highly visible notification. If the user is relaxed, it can provide a notification with more detailed information. Furthermore, if the user is in a hurry, it can provide a notification that gets straight to the point. By adjusting notification methods according to the user's emotions, more appropriate notifications can be delivered.

[0128] Business support systems can assign tasks while considering the user's geographical location. For example, if a user is in a specific location, tasks related to that location can be assigned preferentially. Tasks related to locations close to the user's current location can also be assigned preferentially. Furthermore, tasks can be assigned in a way that minimizes travel time based on the user's geographical location. This allows for optimal task assignment by considering geographical location.

[0129] Business support systems can estimate a user's emotions and adjust scheduling methods based on those estimates. For example, if a user is stressed, the system can provide a simple and intuitive scheduling method. If the user is relaxed, it can provide more detailed scheduling options. Furthermore, if the user is in a hurry, it can provide a way to quickly schedule tasks. By adjusting scheduling methods according to the user's emotions, more appropriate scheduling can be achieved.

[0130] The business support system can analyze users' social media activity and assign relevant tasks. For example, it can prioritize tasks related to a user's current interests based on their social media activity. It can also analyze a user's social media activity and filter and assign relevant tasks. Furthermore, it can perform the most efficient task assignment based on the user's social media activity. In this way, optimal task assignment can be achieved by analyzing social media activity.

[0131] Business support systems can estimate user emotions and adjust automation methods based on those estimates. For example, if a user is stressed, the system can provide a simple and intuitive automation method. If the user is relaxed, it can provide more detailed configuration options. Furthermore, if the user is in a hurry, it can provide a method for rapid automation. By adjusting automation methods according to user emotions, more appropriate automation can be achieved.

[0132] The business support system can suggest the optimal meeting time by referring to the user's past schedule history. For example, it can suggest the most convenient time based on past schedule history. It can also analyze past schedule history and suggest times that avoid overlaps. Furthermore, it can suggest the optimal meeting time based on past schedule history. In this way, the system can suggest the optimal meeting time by referring to past schedule history.

[0133] Business support systems can estimate a user's emotions and prioritize notifications based on those emotions. For example, if a user is stressed, the system can prioritize high-priority notifications. If the user is relaxed, notifications can be delivered with normal priority. Furthermore, if the user is in a hurry, urgent notifications can be given top priority. In this way, important notifications can be prioritized by determining notification priorities according to the user's emotions.

[0134] The business support system can customize schedules based on the user's current work situation. For example, it can suggest optimal meeting times considering the current workload. It can also adjust schedules based on the current work situation. Furthermore, it can analyze the current work situation and suggest the most efficient schedule. This allows for efficient scheduling by customizing schedules based on the current work situation.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The reception desk accepts voice input. For example, it can accept voice input using a microphone, or it can convert voice input to text using specific speech recognition technology. Step 2: The analysis unit analyzes the voice instructions received by the reception unit. The voice instructions are converted into text, and natural language processing technology is used to identify the appropriate action. Step 3: The scheduling unit schedules the meeting based on the instructions analyzed by the analysis unit. It checks the participants' schedules, suggests the optimal time, and integrates with the calendar application. Step 4: The task assignment unit assigns tasks based on the instructions analyzed by the analysis unit. They consider each member's workload and skill set and assign tasks to the appropriate members. Step 5: The automation unit automates the progress management of schedules and tasks set by the scheduling unit and task assignment unit. It automates regular schedule setting and task progress management, and prioritizes tasks. Step 6: The notification unit sends reminders and notifications for schedules and tasks managed by the automation unit. It sends reminders for important tasks and notifications based on the progress of those tasks.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, analysis unit, schedule setting unit, task assignment unit, automation unit, and notification unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can receive voice input using the microphone 38B of the smart device 14. The analysis unit converts voice instructions into text using the identification processing unit 290 of the data processing unit 12 and identifies the appropriate action. The schedule setting unit schedules meetings using the identification processing unit 290 of the data processing unit 12. The task assignment unit assigns tasks using the identification processing unit 290 of the data processing unit 12. The automation unit automates schedule and task progress management using the identification processing unit 290 of the data processing unit 12. The notification unit can send reminders and notifications using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, schedule setting unit, task assignment unit, automation unit, and notification unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can receive voice input using the microphone 238 of the smart glasses 214. The analysis unit converts voice instructions into text using the identification processing unit 290 of the data processing unit 12 and identifies the appropriate action. The schedule setting unit schedules meetings using the identification processing unit 290 of the data processing unit 12. The task assignment unit assigns tasks using the identification processing unit 290 of the data processing unit 12. The automation unit automates schedule and task progress management using the identification processing unit 290 of the data processing unit 12. The notification unit can send reminders and notifications using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, analysis unit, schedule setting unit, task assignment unit, automation unit, and notification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can receive voice input using the microphone 238 of the headset terminal 314. The analysis unit converts voice instructions into text using, for example, the identification processing unit 290 of the data processing unit 12 and identifies the appropriate action. The schedule setting unit schedules meetings using, for example, the identification processing unit 290 of the data processing unit 12. The task assignment unit assigns tasks using, for example, the identification processing unit 290 of the data processing unit 12. The automation unit automates schedule and task progress management using, for example, the identification processing unit 290 of the data processing unit 12. The notification unit can send reminders and notifications using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the reception unit, analysis unit, schedule setting unit, task assignment unit, automation unit, and notification unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit can receive voice input using the microphone 238 of the robot 414. The analysis unit converts voice instructions into text and identifies appropriate actions, for example, by the identification processing unit 290 of the data processing unit 12. The schedule setting unit schedules meetings, for example, by the identification processing unit 290 of the data processing unit 12. The task assignment unit assigns tasks, for example, by the identification processing unit 290 of the data processing unit 12. The automation unit automates schedule and task progress management, for example, by the identification processing unit 290 of the data processing unit 12. The notification unit can send reminders and notifications, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A reception desk that accepts voice input, An analysis unit that analyzes voice instructions received by the reception unit, A scheduling unit that sets the meeting schedule based on the instructions analyzed by the aforementioned analysis unit, A task assignment unit that assigns tasks based on instructions analyzed by the analysis unit, An automation unit that automates the progress management of schedules and tasks set by the schedule setting unit and the task assignment unit, The system includes a notification unit that sends reminders and notifications for schedules and tasks managed by the automation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Accepts voice input The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Convert voice commands to text and identify the appropriate action. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned schedule setting unit, Check the participants' schedules and suggest a suitable time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The task assignment unit, Assign tasks to the appropriate members based on each member's workload. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned automation unit, Automate regular scheduling and task progress management. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, Send reminders and notifications for important tasks. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system analyzes the user's past voice input history and selects the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving voice input, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice input to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving voice input, the system prioritizes accepting input that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving voice input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the voice command analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing voice commands, the level of detail in the analysis is adjusted based on the importance of the command. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing voice commands, different analysis algorithms are applied depending on the category of the command. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing voice commands, the analysis priority is determined based on when the commands were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When analyzing voice commands, the order of analysis is adjusted based on the relevance of the commands. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned schedule setting unit, It estimates the user's emotions and adjusts the scheduling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned schedule setting unit, When scheduling, the system will refer to participants' past schedule history to suggest the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned schedule setting unit, When scheduling, customize the schedule based on the participants' current work status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned schedule setting unit, It estimates the user's emotions and determines the priority of scheduling based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned schedule setting unit, When scheduling, we will suggest the optimal time considering the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned schedule setting unit, When scheduling, we analyze participants' social media activity and suggest a schedule. The system described in Appendix 1, characterized by the features described herein. (Note 26) The task assignment unit, It estimates the user's emotions and adjusts the task assignment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The task assignment unit, When assigning tasks, we refer to each member's past work history to assign tasks to the most suitable member. The system described in Appendix 1, characterized by the features described herein. (Note 28) The task assignment unit, When assigning tasks, customize them based on each member's current workload. The system described in Appendix 1, characterized by the features described herein. (Note 29) The task assignment unit, It estimates the user's emotions and determines the priority of task assignments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The task assignment unit, When assigning tasks, we consider each member's geographical location to assign tasks to the most suitable member. The system described in Appendix 1, characterized by the features described herein. (Note 31) The task assignment unit, When assigning tasks, analyze each member's social media activity before assigning tasks. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned automation unit, It estimates the user's emotions and adjusts the automation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned automation unit, When automating processes, the system selects the optimal automation method by referring to past automation history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned automation unit, When automating, customize the automation methods based on the current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned automation unit, It estimates user emotions and determines automation priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned automation unit, When automating processes, the optimal automation method is selected by considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned automation unit, During automation, we analyze social media activity and propose automation methods. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned notification unit, When sending a notification, the system will refer to past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned notification unit, When sending notifications, customize the notification method based on the current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned notification unit, When sending notifications, we analyze social media activity and suggest notification methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 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 reception desk that accepts voice input, An analysis unit that analyzes voice instructions received by the reception unit, A scheduling unit that sets the meeting schedule based on the instructions analyzed by the aforementioned analysis unit, A task assignment unit that assigns tasks based on instructions analyzed by the analysis unit, An automation unit that automates the progress management of schedules and tasks set by the schedule setting unit and the task assignment unit, The system includes a notification unit that sends reminders and notifications for schedules and tasks managed by the automation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Convert voice commands to text and identify the appropriate action. The system according to feature 1.

3. The aforementioned schedule setting unit, Check the participants' schedules and suggest a suitable time. The system according to feature 1.

4. The task assignment unit, Assign tasks to the appropriate members based on each member's workload. The system according to feature 1.

5. The aforementioned automation unit, Automate regular scheduling and task progress management. The system according to feature 1.

6. The aforementioned notification unit, Send reminders and notifications for important tasks. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system according to feature 1.

Images