System

Abstract

An object of a system according to an embodiment is to generate an efficient appointment schedule in consideration of employee requests and facility maintenance information, and to make an optimal appointment proposal.SOLUTION: A system includes a demand input unit, a maintenance information input unit, a schedule generation unit, and a proposal unit. The request input unit inputs a request of an employee. The maintenance information input unit inputs maintenance information of the equipment. The schedule generation part generates an efficient reservation schedule on the basis of information inputted by the demand input part and the maintenance information input part. The proposal unit makes an optimal reservation proposal based on the reservation schedule generated by the schedule generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, managing office equipment reservations is complicated, and it is difficult to generate efficient reservation schedules that take into account employee requests and equipment maintenance information.

[0005] The system according to the embodiment aims to generate an efficient reservation schedule taking into consideration the requests of employees and equipment maintenance information, and to make optimal reservation proposals. [Means for solving the problem]

[0006] The system according to the embodiment includes a request input unit, a maintenance information input unit, a schedule generation unit, and a proposal unit. The request input unit inputs employee requests. The maintenance information input unit inputs equipment maintenance information. The schedule generation unit generates an efficient reservation schedule based on the information input by the request input unit and the maintenance information input unit. The proposal unit makes optimal reservation proposals based on the reservation schedule generated by the schedule generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an efficient reservation schedule taking into consideration the requests of employees and equipment maintenance information, and can make optimal reservation proposals. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. 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, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The event and facility reservation system according to an embodiment of the present invention is a system that automatically generates an efficient reservation schedule by taking into account employee requests and facility maintenance information, and proposes optimal reservations based on data analysis. As a result, the event and facility reservation system can improve the efficiency and management of office facility reservations.

[0029] An event and facility reservation system according to an embodiment includes a request input unit, a maintenance information input unit, a schedule generation unit, and a proposal unit. The request input unit inputs employee requests. For example, employees can input the purpose of a meeting, the number of participants, necessary equipment, and desired time slots. The maintenance information input unit inputs equipment maintenance information. For example, employees can input a conference room cleaning schedule and equipment inspection dates. The schedule generation unit generates an efficient reservation schedule based on the information input by the request input unit and the maintenance information input unit. For example, the schedule generation unit adjusts the schedule so that multiple meetings do not overlap and proposes the most suitable conference room. The proposal unit makes optimal reservation proposals based on the reservation schedule generated by the schedule generation unit. For example, if conference rooms are heavily used during a certain time period, the proposal unit proposes to avoid that time period. This enables the event and facility reservation system according to an embodiment to achieve more efficient office facility reservation and improved management.

[0030] The request input unit can automatically specify vague requests using natural language processing. For example, the request input unit uses natural language processing technology to analyze vague requests entered by employees and convert them into specific content. For example, a request such as "I want to have a meeting" can be specified as "A conference room that can accommodate 10 people, with a projector and a whiteboard." The request input unit also uses natural language processing to understand the context of the employee's request and suggest appropriate facilities and time slots. For example, a request such as "I want to have a meeting next afternoon" can be specified as "A conference room from 2:00 to 4:00 PM next Wednesday." When a vague request is entered, the request input unit's generation AI automatically generates questions to collect specific information. For example, in response to a request such as "I want to have a meeting," the unit asks, "How many people will be attending?" to elicit a more specific request. This allows for more accurate reservation schedules to be generated by specifying vague requests.

[0031] The request input unit can learn employees' past behavioral patterns and automatically suggest additional requests that are predicted based on the input request. The request input unit, for example, learns employees' past conference reservation data and suggests additional requests that are predicted based on the input request. For example, it suggests using a projector for an employee who has frequently used a projector in the past. The request input unit also analyzes employees' behavioral patterns and suggests related equipment and time periods based on the input request. For example, it suggests that time period for an employee who has held meetings during a specific time period in the past. The request input unit also uses the generation AI to ask follow-up questions to the input request based on the employee's past behavioral patterns to elicit specific requests. For example, in response to a request to "hold a meeting," it asks, "Is there any equipment that has been used in the past?" This makes it possible to learn past behavioral patterns and make more appropriate reservation suggestions.

[0032] The request input unit uses voice input to enable employees to verbally input their requests and schedules, reducing the effort required for input. The request input unit, for example, uses voice recognition technology to build a system that allows employees to verbally input their requests and schedules. For example, a user might verbally input, "I would like to book a meeting for next Monday." The request input unit also uses voice input to enable employees to input requests and schedules without using their hands. For example, voice input is performed using a smartphone or smart speaker. The request input unit also converts the voice input into text in real time, and a generation AI proposes the optimal schedule based on that information. For example, a user might verbally input, "I would like to book a meeting from 2:00 to 3:00 p.m." This reduces the effort required for input by using voice input.

[0033] The request input unit can work with other work management systems to automatically import employee work schedules, reducing the need for manual input. The request input unit can, for example, work with other work management systems to build a system that automatically imports employee work schedules. For example, it can work with Google Calendar or Outlook. The request input unit can also automatically retrieve schedule data from the work management system, and the generation AI can propose the optimal reservation schedule based on that information. For example, it can automatically import existing meeting schedules. The request input unit can also work with other work management systems to reduce the effort required for employees to manually enter schedules. For example, it can automatically retrieve schedule data from a project management tool. In this way, by working with other work management systems, the effort required for manual input can be reduced.

[0034] The maintenance information input unit can refer to past maintenance history and predict the next maintenance date. For example, the maintenance information input unit stores the equipment's past maintenance history in a database, and the generation AI predicts the next maintenance date based on that data. For example, it automatically generates regular inspection schedules. The maintenance information input unit also analyzes the maintenance history and predicts the equipment's deterioration trends to suggest the next maintenance date. For example, it advances the maintenance date for frequently used equipment. The maintenance information input unit also uses the generation AI to predict the next maintenance date based on past maintenance data and notify the equipment manager. For example, it notifies the equipment manager in advance of when maintenance is required. This makes it possible to predict the next maintenance date by referring to past maintenance history.

[0035] The maintenance information input unit can monitor the frequency of equipment use and deterioration status in real time, and automatically determine the need for maintenance. For example, the maintenance information input unit installs sensors that monitor the frequency of equipment use and deterioration status in real time, and the generation AI determines the need for maintenance based on that data. For example, it monitors usage time and temperature changes. The maintenance information input unit also analyzes data collected in real time and builds a system that evaluates the deterioration status of equipment. For example, it detects changes in vibration and sound to determine deterioration. The maintenance information input unit also monitors the frequency of equipment use and deterioration status in real time, and develops a system that automatically notifies when maintenance is required. For example, it issues an alert if an abnormality is detected. This makes it possible to automatically determine the need for maintenance through real-time monitoring.

[0036] The maintenance information input unit can link with other building management systems to improve the efficiency of overall building management. The maintenance information input unit, for example, links equipment maintenance information with other building management systems to build a system that improves the efficiency of overall building management. For example, it links with air conditioning systems and lighting systems. The maintenance information input unit also automatically obtains maintenance information from other building management systems, and the generation AI proposes an optimal maintenance schedule based on that information. For example, it integrates maintenance plans for the entire building. The maintenance information input unit also links equipment maintenance information with other building management systems to avoid duplication of maintenance work and generate an efficient schedule. For example, it adjusts cleaning schedules and inspection schedules. In this way, by linking with other building management systems, the efficiency of overall building management is improved.

[0037] The maintenance information input unit can visualize maintenance information and provide a dashboard that can be intuitively understood by facility managers. The maintenance information input unit, for example, builds a system that visualizes maintenance information and provides a dashboard that can be intuitively understood by facility managers. For example, it displays the maintenance status using graphs and charts. The maintenance information input unit also updates the maintenance information of the facility in real time and displays it on the dashboard. For example, it displays the latest maintenance history and the next maintenance schedule. The maintenance information input unit also provides a function that allows the dashboard to be customized, allowing the facility manager to intuitively display the information they need. For example, it highlights the maintenance status of specific facility. This visualizes the maintenance information, allowing the facility manager to intuitively understand it.

[0038] The schedule generation unit can refer to past reservation data and learn optimal patterns. The schedule generation unit, for example, analyzes past reservation data and builds a system that learns optimal reservation patterns. For example, it learns that meetings tend to be concentrated during certain time periods and suggests avoiding those times. The schedule generation unit also uses a generation AI to automatically generate optimal reservation schedules based on past reservation data. For example, it suggests optimal conference rooms and time periods based on past data. The schedule generation unit also refers to past reservation data and the generation AI learns optimal reservation patterns to propose efficient schedules. For example, if a particular piece of equipment is used frequently, it will prioritize reservations for that equipment. In this way, optimal patterns can be learned by referring to past reservation data.

[0039] The schedule generation unit can collect employee feedback on the schedule it generates and reflect it in the next schedule generation. For example, the schedule generation unit builds a system that collects feedback from employees on the schedule generated by the generation AI and reflects the results in the next schedule generation. For example, it uses online surveys and comment functions. The schedule generation unit also analyzes employee feedback in real time, and the generation AI reflects it in the next schedule generation. For example, it adjusts the schedule based on employee opinions. The schedule generation unit also collects employee feedback on the schedule generated by the generation AI and uses that data to improve the next schedule generation. For example, it suggests the optimal conference room and time slot based on the feedback. In this way, the next schedule generation is improved by reflecting employee feedback.

[0040] The schedule generation unit can link the generated schedule with other business management systems to improve overall business efficiency. For example, the schedule generation unit links the schedule generated by the generation AI with other business management systems to build a system that improves overall business efficiency. For example, it links with project management tools and task management tools. The schedule generation unit also automatically obtains schedule data from other business management systems, and the generation AI proposes the optimal reservation schedule based on that information. For example, it automatically imports existing task schedules. The schedule generation unit also links the schedule generated by the generation AI with other business management systems, reducing the effort required for employees to manually adjust schedules. For example, it automatically adjusts the schedule according to the progress of the project. In this way, by linking with other business management systems, overall business efficiency is improved.

[0041] The schedule generation unit can visualize the schedule it generates and provide a dashboard that employees can intuitively understand. The schedule generation unit, for example, builds a system that visualizes the schedule generated by the generation AI and provides a dashboard that employees can intuitively understand. For example, it displays the schedule using graphs and charts. The schedule generation unit also updates the schedule generated by the generation AI in real time and displays it on a dashboard. For example, it displays the latest schedule and changes. The schedule generation unit also provides a function to customize the dashboard, allowing employees to intuitively display the information they need. For example, it highlights the schedules of specific meetings or facilities. In this way, the visualized schedule allows employees to intuitively understand it.

[0042] The suggestion unit can analyze past reservation data and automatically detect specific patterns and trends. For example, the suggestion unit builds a system that analyzes past reservation data and automatically detects specific patterns and trends. For example, it detects a tendency for meetings to be concentrated during specific time periods. The suggestion unit also has the generation AI detect specific patterns and trends based on past reservation data and make optimal reservation suggestions. For example, if a specific piece of equipment is used frequently, it will prioritize reserving that equipment. The suggestion unit also references past reservation data and has the generation AI detect specific patterns and trends to make efficient reservation suggestions. For example, if conference room usage is concentrated during a specific time period, it will suggest avoiding that time period. In this way, specific patterns and trends can be automatically detected by analyzing past reservation data.

[0043] The suggestion unit can collect employee feedback on proposed reservations and reflect it in the next proposal. For example, the suggestion unit builds a system that collects feedback from employees on reservations proposed by the generation AI and reflects the results in the next proposal. For example, it uses online surveys and comment functions. The suggestion unit also analyzes employee feedback in real time, which the generation AI reflects in the next reservation proposal. For example, it adjusts reservations based on employee opinions. The suggestion unit also collects employee feedback on reservations proposed by the generation AI and uses that data to improve the next reservation proposal. For example, it suggests the optimal conference room and time slot based on the feedback. In this way, the next proposal is improved by reflecting employee feedback.

[0044] The proposal unit can link the proposed reservations with other business management systems to improve overall business efficiency. For example, the proposal unit links the reservations proposed by the generation AI with other business management systems to build a system that improves overall business efficiency. For example, it links with project management tools and task management tools. The proposal unit also automatically obtains reservation data from other business management systems, and the generation AI makes optimal reservation proposals based on that information. For example, it automatically imports existing task schedules. The proposal unit also links the reservations proposed by the generation AI with other business management systems, reducing the effort required for employees to manually adjust reservations. For example, it automatically adjusts reservations according to the progress of a project. In this way, by linking with other business management systems, overall business efficiency is improved.

[0045] The suggestion unit can visualize the proposed reservations and provide a dashboard that employees can intuitively understand. For example, the suggestion unit builds a system that visualizes the reservations proposed by the generation AI and provides a dashboard that employees can intuitively understand. For example, it displays the reservation status using graphs and charts. The suggestion unit also updates the reservations proposed by the generation AI in real time and displays them on the dashboard. For example, it displays the latest reservation status and changes. The suggestion unit also provides a function to customize the dashboard, allowing employees to intuitively display the information they need. For example, it highlights the reservation status of specific meetings or facilities. In this way, the proposed reservations are visualized, allowing employees to intuitively understand them.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The request input unit can also acquire employee health data and propose an optimal schedule based on the employee's health condition. For example, if an employee is not getting enough rest based on their sleep data, the time of their meeting can be adjusted. The request input unit can also acquire an employee's exercise data and, if there is a concern that they are not getting enough exercise, suggest setting aside breaks between meetings. Furthermore, the request input unit can also propose a schedule that takes into account the time it takes for employees to digest their meals based on their diet data. This allows for the proposal of an optimal schedule that takes into account the employee's health condition.

[0048] The request input unit can also propose a relaxing environment taking into consideration the hobbies and interests of employees. For example, for an employee who likes music, it can suggest playing relaxing music during meetings. For an employee who likes nature, it can also suggest a conference room with a good view from the window. Furthermore, for an employee who likes art, it can also suggest displaying artwork in the conference room. In this way, a relaxing environment that takes into consideration the hobbies and interests of employees can be provided.

[0049] The request input section can also propose schedules that reflect improvements based on employees' feedback on past meetings. For example, if there was not enough time in a past meeting, it can suggest extending the next meeting. Also, if there was a lack of specific equipment in a past meeting, it can suggest adding that equipment to the next meeting. Furthermore, if there were too many participants in a past meeting, it can suggest limiting the number of participants in the next meeting. This allows the system to propose an optimal schedule that reflects past feedback.

[0050] The request input unit uses voice input to enable employees to verbally input requests and schedules, reducing the effort required for input. For example, employees can verbally input, "I would like to book a meeting for next Monday." The request input unit also uses voice input to enable employees to input requests and schedules without using their hands. For example, voice input can be performed using a smartphone or smart speaker. The request input unit also converts voice input into text in real time, and the generation AI proposes the optimal schedule based on that information. For example, employees can verbally input, "I would like to book a meeting from 2:00 to 3:00 p.m." This reduces the effort required for input by using voice input.

[0051] The request input unit can link with other work management systems and automatically import employee work schedules, reducing the need for manual input. For example, it can link with Google Calendar or Outlook. The request input unit also automatically retrieves schedule data from the work management system, and the generation AI uses that information to propose the optimal reservation schedule. For example, it can automatically import existing meeting schedules. The request input unit can also link with other work management systems, reducing the effort required for employees to manually enter schedules. For example, it can automatically retrieve schedule data from a project management tool. In this way, linking with other work management systems reduces the effort required for manual input.

[0052] The maintenance information input unit can refer to past maintenance history and predict when the next maintenance will be required. For example, the past maintenance history of equipment is stored in a database, and the generation AI predicts when the next maintenance will be required based on that data. For example, it can automatically generate regular inspection schedules. The maintenance information input unit can also analyze the maintenance history and predict the deterioration trend of the equipment, thereby proposing when the next maintenance will be required. For example, it can advance the maintenance period for equipment that is used frequently. The maintenance information input unit can also use the generation AI to predict when the next maintenance will be required based on past maintenance data, and notify the equipment manager. For example, it can notify the equipment manager in advance when maintenance is required. This makes it possible to predict when the next maintenance will be required by referring to past maintenance history.

[0053] The maintenance information input unit can monitor the frequency of equipment use and deterioration status in real time, and automatically determine the need for maintenance. For example, sensors can be installed to monitor the frequency of equipment use and deterioration status in real time, and the generation AI can use that data to determine the need for maintenance. For example, it monitors usage time and temperature changes. The maintenance information input unit can also analyze data collected in real time and build a system to evaluate the deterioration status of equipment. For example, it can detect changes in vibration and sound to determine deterioration. The maintenance information input unit can also monitor the frequency of equipment use and deterioration status in real time, and develop a system that automatically notifies when maintenance is required. For example, it can issue an alert if an abnormality is detected. This makes it possible to automatically determine the need for maintenance through real-time monitoring.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The request input section inputs the employee's requests. For example, the employee can input the purpose of the meeting, the number of participants, the necessary equipment, the desired time slot, etc. Step 2: The maintenance information input unit inputs equipment maintenance information, such as the cleaning schedule for the conference room and the date of equipment inspection. Step 3: The schedule generation unit generates an efficient reservation schedule based on the information input by the request input unit and the maintenance information input unit. For example, it adjusts the schedule so that multiple meetings do not overlap, and suggests the most suitable conference room. Step 4: The suggestion unit makes optimal reservation suggestions based on the reservation schedule generated by the schedule generation unit. For example, if a conference room is heavily used during a specific time period, the suggestion unit suggests avoiding that time period.

[0056] (Example 2) The event and facility reservation system according to an embodiment of the present invention is a system that automatically generates an efficient reservation schedule by taking into account employee requests and facility maintenance information, and proposes optimal reservations based on data analysis. As a result, the event and facility reservation system can improve the efficiency and management of office facility reservations.

[0057] An event and facility reservation system according to an embodiment includes a request input unit, a maintenance information input unit, a schedule generation unit, and a proposal unit. The request input unit inputs employee requests. For example, employees can input the purpose of a meeting, the number of participants, necessary equipment, and desired time slots. The maintenance information input unit inputs equipment maintenance information. For example, employees can input a conference room cleaning schedule and equipment inspection dates. The schedule generation unit generates an efficient reservation schedule based on the information input by the request input unit and the maintenance information input unit. For example, the schedule generation unit adjusts the schedule so that multiple meetings do not overlap and proposes the most suitable conference room. The proposal unit makes optimal reservation proposals based on the reservation schedule generated by the schedule generation unit. For example, if conference rooms are heavily used during a certain time period, the proposal unit proposes to avoid that time period. This enables the event and facility reservation system according to an embodiment to achieve more efficient office facility reservation and improved management.

[0058] The request input unit can automatically specify vague requests using natural language processing. For example, the request input unit uses natural language processing technology to analyze vague requests entered by employees and convert them into specific content. For example, a request such as "I want to have a meeting" can be specified as "A conference room that can accommodate 10 people, with a projector and a whiteboard." The request input unit also uses natural language processing to understand the context of the employee's request and suggest appropriate facilities and time slots. For example, a request such as "I want to have a meeting next afternoon" can be specified as "A conference room from 2:00 to 4:00 PM next Wednesday." When a vague request is entered, the request input unit's generation AI automatically generates questions to collect specific information. For example, in response to a request such as "I want to have a meeting," the unit asks, "How many people will be attending?" to elicit a more specific request. This allows for more accurate reservation schedules to be generated by specifying vague requests.

[0059] The request input unit can learn employees' past behavioral patterns and automatically suggest additional requests that are predicted based on the input request. The request input unit, for example, learns employees' past conference reservation data and suggests additional requests that are predicted based on the input request. For example, it suggests using a projector for an employee who has frequently used a projector in the past. The request input unit also analyzes employees' behavioral patterns and suggests related equipment and time periods based on the input request. For example, it suggests that time period for an employee who has held meetings during a specific time period in the past. The request input unit also uses the generation AI to ask follow-up questions to the input request based on the employee's past behavioral patterns to elicit specific requests. For example, in response to a request to "hold a meeting," it asks, "Is there any equipment that has been used in the past?" This makes it possible to learn past behavioral patterns and make more appropriate reservation suggestions.

[0060] The request input unit can use the emotion estimation function to analyze the emotions expressed by employees when they input their requests and propose an optimal schedule for reducing stress. The request input unit, for example, analyzes the emotions expressed by employees when they input their requests in real time and proposes an optimal schedule for reducing stress. For example, if stress is high, it proposes a time period when employees can relax. The request input unit also uses the emotion estimation function to analyze the emotional state of employees and proposes a schedule for eliciting positive emotions. For example, if employees are feeling depressed, it proposes a short meeting. The request input unit also provides an interface for reducing stress based on the employee's emotional data and proposes an optimal schedule. For example, if employees are feeling excited, it proposes a relaxing environment. In this way, an optimal schedule for reducing stress is proposed by analyzing emotions.

[0061] The request input unit uses voice input to enable employees to verbally input their requests and schedules, reducing the effort required for input. The request input unit, for example, uses voice recognition technology to build a system that allows employees to verbally input their requests and schedules. For example, a user might verbally input, "I would like to book a meeting for next Monday." The request input unit also uses voice input to enable employees to input requests and schedules without using their hands. For example, voice input is performed using a smartphone or smart speaker. The request input unit also converts the voice input into text in real time, and a generation AI proposes the optimal schedule based on that information. For example, a user might verbally input, "I would like to book a meeting from 2:00 to 3:00 p.m." This reduces the effort required for input by using voice input.

[0062] The request input unit can work with other work management systems to automatically import employee work schedules, reducing the need for manual input. The request input unit can, for example, work with other work management systems to build a system that automatically imports employee work schedules. For example, it can work with Google Calendar or Outlook. The request input unit can also automatically retrieve schedule data from the work management system, and the generation AI can propose the optimal reservation schedule based on that information. For example, it can automatically import existing meeting schedules. The request input unit can also work with other work management systems to reduce the effort required for employees to manually enter schedules. For example, it can automatically retrieve schedule data from a project management tool. In this way, by working with other work management systems, the effort required for manual input can be reduced.

[0063] The request input unit can use the emotion estimation function to provide real-time feedback on the emotions felt by employees when they enter data, thereby providing an interface that draws out positive emotions. The request input unit, for example, uses the emotion estimation function to analyze the emotions felt by employees when they enter data in real time, thereby providing an interface that draws out positive emotions. For example, an encouraging message can be displayed while the employee is entering data. The request input unit also provides real-time feedback on the employee's emotional state, thereby providing an interface that draws out positive emotions. For example, if the employee is feeling depressed, relaxing background music can be played. The request input unit also provides real-time feedback on the emotions felt by employees when they enter data, based on the emotion estimation data, thereby providing advice to draw out positive emotions. For example, appropriate words of encouragement can be displayed according to the input content. In this way, an interface that draws out positive emotions by providing real-time feedback on emotions can be provided.

[0064] The maintenance information input unit can refer to past maintenance history and predict the next maintenance date. For example, the maintenance information input unit stores the equipment's past maintenance history in a database, and the generation AI predicts the next maintenance date based on that data. For example, it automatically generates regular inspection schedules. The maintenance information input unit also analyzes the maintenance history and predicts the equipment's deterioration trends to suggest the next maintenance date. For example, it advances the maintenance date for frequently used equipment. The maintenance information input unit also uses the generation AI to predict the next maintenance date based on past maintenance data and notify the equipment manager. For example, it notifies the equipment manager in advance of when maintenance is required. This makes it possible to predict the next maintenance date by referring to past maintenance history.

[0065] The maintenance information input unit can monitor the frequency of equipment use and deterioration status in real time, and automatically determine the need for maintenance. For example, the maintenance information input unit installs sensors that monitor the frequency of equipment use and deterioration status in real time, and the generation AI determines the need for maintenance based on that data. For example, it monitors usage time and temperature changes. The maintenance information input unit also analyzes data collected in real time and builds a system that evaluates the deterioration status of equipment. For example, it detects changes in vibration and sound to determine deterioration. The maintenance information input unit also monitors the frequency of equipment use and deterioration status in real time, and develops a system that automatically notifies when maintenance is required. For example, it issues an alert if an abnormality is detected. This makes it possible to automatically determine the need for maintenance through real-time monitoring.

[0066] The maintenance information input unit can use the emotion estimation function to analyze the emotions of the facility manager and propose an optimal schedule for reducing stress in the maintenance plan. The maintenance information input unit, for example, analyzes the emotions of the facility manager in real time and proposes an optimal maintenance schedule for reducing stress. For example, if the facility manager is emotionally charged, the maintenance information input unit distributes maintenance work. The maintenance information input unit also uses the emotion estimation function to analyze the facility manager's emotional state and provide an interface for reducing stress. For example, it proposes a relaxing environment. The maintenance information input unit also automatically generates a maintenance schedule for reducing stress based on the facility manager's emotional data. For example, it concentrates maintenance work during times when the facility manager is emotionally calm. In this way, an optimal schedule for reducing stress is proposed by analyzing emotions.

[0067] The maintenance information input unit can link with other building management systems to improve the efficiency of overall building management. The maintenance information input unit, for example, links equipment maintenance information with other building management systems to build a system that improves the efficiency of overall building management. For example, it links with air conditioning systems and lighting systems. The maintenance information input unit also automatically obtains maintenance information from other building management systems, and the generation AI proposes an optimal maintenance schedule based on that information. For example, it integrates maintenance plans for the entire building. The maintenance information input unit also links equipment maintenance information with other building management systems to avoid duplication of maintenance work and generate an efficient schedule. For example, it adjusts cleaning schedules and inspection schedules. In this way, by linking with other building management systems, the efficiency of overall building management is improved.

[0068] The maintenance information input unit can visualize maintenance information and provide a dashboard that can be intuitively understood by facility managers. The maintenance information input unit, for example, builds a system that visualizes maintenance information and provides a dashboard that can be intuitively understood by facility managers. For example, it displays the maintenance status using graphs and charts. The maintenance information input unit also updates the maintenance information of the facility in real time and displays it on the dashboard. For example, it displays the latest maintenance history and the next maintenance schedule. The maintenance information input unit also provides a function that allows the dashboard to be customized, allowing the facility manager to intuitively display the information they need. For example, it highlights the maintenance status of specific facility. This visualizes the maintenance information, allowing the facility manager to intuitively understand it.

[0069] The maintenance information input unit can use the emotion estimation function to provide an interface that feeds back the emotions of the facility manager in real time and draws out positive emotions. The maintenance information input unit, for example, uses the emotion estimation function to analyze the emotions of the facility manager in real time and provide an interface that draws out positive emotions. For example, if the facility manager is feeling depressed, an encouraging message is displayed. The maintenance information input unit also provides an interface that feeds back the emotional state of the facility manager in real time and draws out positive emotions. For example, if the facility manager is feeling excited, relaxing background music is played. The maintenance information input unit also feeds back the emotions of the facility manager in real time based on the emotion estimation data and provides advice to draw out positive emotions. For example, concentrating maintenance work on times when the facility manager is feeling calm. In this way, an interface that draws out positive emotions by feeding back emotions in real time is provided.

[0070] The schedule generation unit can refer to past reservation data and learn optimal patterns. The schedule generation unit, for example, analyzes past reservation data and builds a system that learns optimal reservation patterns. For example, it learns that meetings tend to be concentrated during certain time periods and suggests avoiding those times. The schedule generation unit also uses a generation AI to automatically generate optimal reservation schedules based on past reservation data. For example, it suggests optimal conference rooms and time periods based on past data. The schedule generation unit also refers to past reservation data and the generation AI learns optimal reservation patterns to propose efficient schedules. For example, if a particular piece of equipment is used frequently, it will prioritize reservations for that equipment. In this way, optimal patterns can be learned by referring to past reservation data.

[0071] The schedule generation unit can collect employee feedback on the schedule it generates and reflect it in the next schedule generation. For example, the schedule generation unit builds a system that collects feedback from employees on the schedule generated by the generation AI and reflects the results in the next schedule generation. For example, it uses online surveys and comment functions. The schedule generation unit also analyzes employee feedback in real time, and the generation AI reflects it in the next schedule generation. For example, it adjusts the schedule based on employee opinions. The schedule generation unit also collects employee feedback on the schedule generated by the generation AI and uses that data to improve the next schedule generation. For example, it suggests the optimal conference room and time slot based on the feedback. In this way, the next schedule generation is improved by reflecting employee feedback.

[0072] The schedule generation unit can use the emotion estimation function to analyze employees' emotions and propose an optimal schedule for reducing stress. The schedule generation unit, for example, analyzes employees' emotions in real time and proposes an optimal schedule for reducing stress. For example, if an employee is emotionally charged, it proposes a time period in which they can relax. The schedule generation unit also uses the emotion estimation function to analyze an employee's emotional state and proposes a schedule for eliciting positive emotions. For example, if an employee is emotionally charged, it proposes a short meeting. The schedule generation unit also provides an interface for reducing stress based on the employee's emotional data and proposes an optimal schedule. For example, if an employee is emotionally charged, it proposes a relaxing environment. In this way, an optimal schedule for reducing stress is proposed by analyzing emotions.

[0073] The schedule generation unit can link the generated schedule with other business management systems to improve overall business efficiency. For example, the schedule generation unit links the schedule generated by the generation AI with other business management systems to build a system that improves overall business efficiency. For example, it links with project management tools and task management tools. The schedule generation unit also automatically obtains schedule data from other business management systems, and the generation AI proposes the optimal reservation schedule based on that information. For example, it automatically imports existing task schedules. The schedule generation unit also links the schedule generated by the generation AI with other business management systems, reducing the effort required for employees to manually adjust schedules. For example, it automatically adjusts the schedule according to the progress of the project. In this way, by linking with other business management systems, overall business efficiency is improved.

[0074] The schedule generation unit can visualize the schedule it generates and provide a dashboard that employees can intuitively understand. The schedule generation unit, for example, builds a system that visualizes the schedule generated by the generation AI and provides a dashboard that employees can intuitively understand. For example, it displays the schedule using graphs and charts. The schedule generation unit also updates the schedule generated by the generation AI in real time and displays it on a dashboard. For example, it displays the latest schedule and changes. The schedule generation unit also provides a function to customize the dashboard, allowing employees to intuitively display the information they need. For example, it highlights the schedules of specific meetings or facilities. In this way, the visualized schedule allows employees to intuitively understand it.

[0075] The schedule generation unit can use the emotion estimation function to provide an interface that feeds back the employee's emotions in real time and draws out positive emotions. The schedule generation unit, for example, uses the emotion estimation function to analyze the employee's emotions in real time and provide an interface that draws out positive emotions. For example, if the employee is feeling depressed, an encouraging message is displayed. The schedule generation unit also provides an interface that feeds back the employee's emotional state in real time and draws out positive emotions. For example, if the employee is feeling excited, relaxing background music is played. The schedule generation unit also provides feedback on the emotions the employee is feeling when entering data in real time based on the emotion estimation data and provides advice to draw out positive emotions. For example, appropriate words of encouragement are displayed according to the input content. In this way, an interface that draws out positive emotions by feeding back emotions in real time is provided.

[0076] The suggestion unit can analyze past reservation data and automatically detect specific patterns and trends. For example, the suggestion unit builds a system that analyzes past reservation data and automatically detects specific patterns and trends. For example, it detects a tendency for meetings to be concentrated during specific time periods. The suggestion unit also has the generation AI detect specific patterns and trends based on past reservation data and make optimal reservation suggestions. For example, if a specific piece of equipment is used frequently, it will prioritize reserving that equipment. The suggestion unit also references past reservation data and has the generation AI detect specific patterns and trends to make efficient reservation suggestions. For example, if conference room usage is concentrated during a specific time period, it will suggest avoiding that time period. In this way, specific patterns and trends can be automatically detected by analyzing past reservation data.

[0077] The suggestion unit can collect employee feedback on proposed reservations and reflect it in the next proposal. For example, the suggestion unit builds a system that collects feedback from employees on reservations proposed by the generation AI and reflects the results in the next proposal. For example, it uses online surveys and comment functions. The suggestion unit also analyzes employee feedback in real time, which the generation AI reflects in the next reservation proposal. For example, it adjusts reservations based on employee opinions. The suggestion unit also collects employee feedback on reservations proposed by the generation AI and uses that data to improve the next reservation proposal. For example, it suggests the optimal conference room and time slot based on the feedback. In this way, the next proposal is improved by reflecting employee feedback.

[0078] The suggestion unit can use the emotion estimation function to analyze employee emotions and make optimal reservation suggestions to bring out positive emotions. The suggestion unit, for example, analyzes employee emotions in real time and makes optimal reservation suggestions to bring out positive emotions. For example, if an employee is emotionally charged, it suggests a time period in which the employee can relax. The suggestion unit also uses the emotion estimation function to analyze an employee's emotional state and makes reservation suggestions to bring out positive emotions. For example, if an employee is emotionally charged, it suggests a short meeting. The suggestion unit also provides an interface to bring out positive emotions based on the employee's emotion data and makes optimal reservation suggestions. For example, if an employee is emotionally charged, it suggests a relaxing environment. In this way, by analyzing emotions, optimal reservation suggestions to bring out positive emotions are made.

[0079] The proposal unit can link the proposed reservations with other business management systems to improve overall business efficiency. For example, the proposal unit links the reservations proposed by the generation AI with other business management systems to build a system that improves overall business efficiency. For example, it links with project management tools and task management tools. The proposal unit also automatically obtains reservation data from other business management systems, and the generation AI makes optimal reservation proposals based on that information. For example, it automatically imports existing task schedules. The proposal unit also links the reservations proposed by the generation AI with other business management systems, reducing the effort required for employees to manually adjust reservations. For example, it automatically adjusts reservations according to the progress of a project. In this way, by linking with other business management systems, overall business efficiency is improved.

[0080] The suggestion unit can visualize the proposed reservations and provide a dashboard that employees can intuitively understand. For example, the suggestion unit builds a system that visualizes the reservations proposed by the generation AI and provides a dashboard that employees can intuitively understand. For example, it displays the reservation status using graphs and charts. The suggestion unit also updates the reservations proposed by the generation AI in real time and displays them on the dashboard. For example, it displays the latest reservation status and changes. The suggestion unit also provides a function to customize the dashboard, allowing employees to intuitively display the information they need. For example, it highlights the reservation status of specific meetings or facilities. In this way, the proposed reservations are visualized, allowing employees to intuitively understand them.

[0081] The suggestion unit can use the emotion estimation function to provide feedback on the employee's emotions in real time and provide an interface that draws out positive emotions. For example, the suggestion unit uses the emotion estimation function to analyze the employee's emotions in real time and provide an interface that draws out positive emotions. For example, if the employee is feeling depressed, an encouraging message is displayed. The suggestion unit also provides an interface that feeds back the employee's emotional state in real time and draws out positive emotions. For example, if the employee is feeling excited, relaxing background music is played. The suggestion unit also provides feedback on the emotions the employee is feeling when entering data in real time based on the emotion estimation data and provides advice to draw out positive emotions. For example, appropriate words of encouragement are displayed according to the input content. In this way, an interface that draws out positive emotions by feeding back emotions in real time is provided.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The request input unit can also acquire employee health data and propose an optimal schedule based on the employee's health condition. For example, if an employee is not getting enough rest based on their sleep data, the time of their meeting can be adjusted. The request input unit can also acquire an employee's exercise data and, if there is a concern that they are not getting enough exercise, suggest setting aside breaks between meetings. Furthermore, the request input unit can also propose a schedule that takes into account the time it takes for employees to digest their meals based on their diet data. This allows for the proposal of an optimal schedule that takes into account the employee's health condition.

[0084] The request input unit can also propose a relaxing environment taking into consideration the hobbies and interests of employees. For example, for an employee who likes music, it can suggest playing relaxing music during meetings. For an employee who likes nature, it can also suggest a conference room with a good view from the window. Furthermore, for an employee who likes art, it can also suggest displaying artwork in the conference room. In this way, a relaxing environment that takes into consideration the hobbies and interests of employees can be provided.

[0085] The request input section can also propose schedules that reflect improvements based on employees' feedback on past meetings. For example, if there was not enough time in a past meeting, it can suggest extending the next meeting. Also, if there was a lack of specific equipment in a past meeting, it can suggest adding that equipment to the next meeting. Furthermore, if there were too many participants in a past meeting, it can suggest limiting the number of participants in the next meeting. This allows the system to propose an optimal schedule that reflects past feedback.

[0086] The request input unit can also use the emotion estimation function to analyze the employee's emotions and suggest relaxation methods to reduce stress. For example, if the employee is feeling emotional, it can suggest deep breathing or meditation. If the employee is feeling depressed, it can suggest light exercise or a walk. Furthermore, if the employee is feeling unstable, it can suggest relaxing music or aromas. In this way, relaxation methods to reduce stress are suggested by analyzing emotions.

[0087] The request input unit uses voice input to enable employees to verbally input requests and schedules, reducing the effort required for input. For example, employees can verbally input, "I would like to book a meeting for next Monday." The request input unit also uses voice input to enable employees to input requests and schedules without using their hands. For example, voice input can be performed using a smartphone or smart speaker. The request input unit also converts voice input into text in real time, and the generation AI proposes the optimal schedule based on that information. For example, employees can verbally input, "I would like to book a meeting from 2:00 to 3:00 p.m." This reduces the effort required for input by using voice input.

[0088] The request input unit can link with other work management systems and automatically import employee work schedules, reducing the need for manual input. For example, it can link with Google Calendar or Outlook. The request input unit also automatically retrieves schedule data from the work management system, and the generation AI uses that information to propose the optimal reservation schedule. For example, it can automatically import existing meeting schedules. The request input unit can also link with other work management systems, reducing the effort required for employees to manually enter schedules. For example, it can automatically retrieve schedule data from a project management tool. In this way, linking with other work management systems reduces the effort required for manual input.

[0089] The request input unit can use the emotion estimation function to provide an interface that elicits positive emotions by providing real-time feedback on the emotions felt by employees when they enter information. For example, it can display encouraging messages while employees are entering information. The request input unit also provides an interface that elicits positive emotions by providing real-time feedback on the emotional state of employees. For example, if the employee is feeling depressed, it can play relaxing background music. The request input unit also provides real-time feedback on the emotions felt by employees when they enter information based on the emotion estimation data, and provides advice to elicit positive emotions. For example, it can display appropriate encouraging words according to the content of the input. This provides an interface that elicits positive emotions by providing real-time feedback on emotions.

[0090] The maintenance information input unit can refer to past maintenance history and predict when the next maintenance will be required. For example, the past maintenance history of equipment is stored in a database, and the generation AI predicts when the next maintenance will be required based on that data. For example, it can automatically generate regular inspection schedules. The maintenance information input unit can also analyze the maintenance history and predict the deterioration trend of the equipment, thereby proposing when the next maintenance will be required. For example, it can advance the maintenance period for equipment that is used frequently. The maintenance information input unit can also use the generation AI to predict when the next maintenance will be required based on past maintenance data, and notify the equipment manager. For example, it can notify the equipment manager in advance when maintenance is required. This makes it possible to predict when the next maintenance will be required by referring to past maintenance history.

[0091] The maintenance information input unit can monitor the frequency of equipment use and deterioration status in real time, and automatically determine the need for maintenance. For example, sensors can be installed to monitor the frequency of equipment use and deterioration status in real time, and the generation AI can use that data to determine the need for maintenance. For example, it monitors usage time and temperature changes. The maintenance information input unit can also analyze data collected in real time and build a system to evaluate the deterioration status of equipment. For example, it can detect changes in vibration and sound to determine deterioration. The maintenance information input unit can also monitor the frequency of equipment use and deterioration status in real time, and develop a system that automatically notifies when maintenance is required. For example, it can issue an alert if an abnormality is detected. This makes it possible to automatically determine the need for maintenance through real-time monitoring.

[0092] The maintenance information input unit can use the emotion estimation function to analyze the emotions of the facility manager and propose an optimal schedule for reducing stress in maintenance planning. For example, it can analyze the emotions of the facility manager in real time and propose an optimal maintenance schedule for reducing stress. For example, if the facility manager is emotional, it can distribute maintenance work. The maintenance information input unit also uses the emotion estimation function to analyze the facility manager's emotional state and provide an interface for reducing stress. For example, it can propose a relaxing environment. The maintenance information input unit also automatically generates a maintenance schedule for reducing stress based on the facility manager's emotional data. For example, it can concentrate maintenance work during times when the manager is emotionally calm. In this way, an optimal schedule for reducing stress is proposed by analyzing emotions.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The request input section inputs the employee's requests. For example, the employee can input the purpose of the meeting, the number of participants, the necessary equipment, the desired time slot, etc. Step 2: The maintenance information input unit inputs equipment maintenance information, such as the cleaning schedule for the conference room and the date of equipment inspection. Step 3: The schedule generation unit generates an efficient reservation schedule based on the information input by the request input unit and the maintenance information input unit. For example, it adjusts the schedule so that multiple meetings do not overlap, and suggests the most suitable conference room. Step 4: The suggestion unit makes optimal reservation suggestions based on the reservation schedule generated by the schedule generation unit. For example, if a conference room is heavily used during a specific time period, the suggestion unit suggests avoiding that time period.

[0095] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] 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.

[0154] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a request input section for inputting employee requests; a maintenance information input unit for inputting equipment maintenance information; a schedule generation unit that generates an efficient reservation schedule based on the information input by the request input unit and the maintenance information input unit; a proposal unit that makes an optimal reservation proposal based on the reservation schedule generated by the schedule generation unit; A system characterized by:

2. The request input unit Automatically clarifying vague requests using natural language processing 2. The system of claim 1.

3. The request input unit Learns employee behavior patterns and automatically suggests additional anticipated requests based on input requests 2. The system of claim 1.

4. The request input unit Analyze employees' emotions as they type and suggest optimal schedules to reduce stress 2. The system of claim 1.

5. The request input unit Using voice input, employees can verbally input requests and schedules, reducing the amount of work required to enter information.

2. The system of claim 1.

6. The request input unit Integrate with other work management systems to automatically capture employee work schedules, reducing the need for manual entry 2. The system of claim 1.

7. The request input unit Provides an interface that provides real-time feedback on employees' emotions as they input data, eliciting positive emotions 2. The system of claim 1.

8. The maintenance information input unit Refer to past maintenance history and predict when the next maintenance will be required 2. The system of claim 1.

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