System

Abstract

An object of the system according to the embodiment is to grasp a use situation of an office in real time and to perform a district proposal and future resource management according to needs of an organization.SOLUTION: A system according to an embodiment includes a database creation unit, a tracking unit, a proposal unit, a simulation unit, and a prediction unit. The database creation unit creates a database of the office information. The tracking unit tracks the use status in real time based on the office information stored in the database by the database unit. The proposal unit makes a compartment proposal according to the needs of the organization on the basis of the use state tracked by the tracking unit. The simulation unit performs simulation of the region proposed by the proposal unit. The prediction unit predicts a future district plan and resource management based on the simulation result obtained by the simulation 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] Conventional technologies do not adequately grasp office usage status in real time, propose zoning plans based on organizational needs, or manage future resources, leaving room for improvement.

[0005] The system according to the embodiment aims to grasp the office usage status in real time, propose zoning according to the needs of the organization, and manage future resources. [Means for solving the problem]

[0006] The system according to the embodiment includes a database unit, a tracking unit, a proposal unit, a simulation unit, and a prediction unit. The database unit creates a database of office information. The tracking unit tracks usage status in real time based on the office information databased by the database unit. The proposal unit proposes partitions according to the needs of the organization based on the usage status tracked by the tracking unit. The simulation unit simulates the partitions proposed by the proposal unit. The prediction unit predicts future partition plans and resource management based on the simulation results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the office usage status in real time, and can propose divisions and manage future resources according to the needs of the organization. [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 office management support system according to an embodiment of the present invention is a system that stores office information in a database, tracks usage in real time, proposes and simulates zoning based on organizational needs, and utilizes predictive models based on big data analysis and machine learning. As a result, the office management support system can improve the efficiency and productivity of office management.

[0029] An office management support system according to an embodiment includes a database creation unit, a tracking unit, a proposal unit, a simulation unit, and a prediction unit. The database creation unit creates a database of office information. For example, it collects information such as the area, facilities, and usage status of each room and stores it in a database. The tracking unit tracks usage status in real time based on the office information created by the database creation unit. For example, it monitors the usage status of each room using sensors, and a generation AI analyzes the data. The proposal unit proposes a layout that meets the needs of the organization based on the usage status tracked by the tracking unit. For example, the generation AI analyzes information such as the organization's business content, number of employees, and necessary facilities, and proposes an optimal layout based on that information. The simulation unit simulates the layout proposed by the proposal unit. For example, it virtually changes the layout and simulates the effects. The prediction unit predicts future layout planning and resource management based on the simulation results obtained by the simulation unit. For example, it uses big data analysis and machine learning to build a prediction model that is useful for future layout planning and resource management. As a result, the office management support system according to an embodiment can achieve improved efficiency and productivity in office management.

[0030] The database creation unit can collect information on the area, facilities, and usage status of each room in the office and store it in a database. For example, when collecting usage status and facility information for each room in the office, the database creation unit analyzes employees' emotional states in real time and adds satisfaction and stress levels to the database. For example, it analyzes employees' facial expressions and voices to calculate an emotional score. When collecting the usage history of each room, it also records employees' emotional data and analyzes emotional patterns during specific time periods or events. For example, it stores stress levels during meetings and satisfaction during breaks in the database. When collecting environmental data (temperature, humidity, lighting, etc.) in the office, it analyzes employees' emotional states and makes suggestions to improve comfort. For example, it records stress levels when the temperature is high and suggests optimal temperature settings. This allows for centralized management of information within the office.

[0031] The tracking unit uses sensors to monitor the usage of each room, and the generated AI can analyze that data. For example, the tracking unit records the usage history of each room in detail and analyzes usage patterns during specific time periods or events. For example, it stores the frequency and usage time of conference rooms in a database and identifies peak times. It also records the usage history of rooms for each event or project and analyzes usage patterns. For example, it stores room usage trends according to the progress of a project in a database. It also analyzes usage patterns during specific time periods or events based on the usage history of each room and proposes optimal room layouts and usage methods. For example, it adjusts the reservation system to match times when conference rooms are most frequently used. This makes it possible to understand office usage in real time.

[0032] The proposal department uses generative AI to analyze information on an organization's business operations, number of employees, and necessary equipment, and can propose the optimal layout based on that information. For example, the proposal department collects environmental data within the office (temperature, humidity, lighting, etc.) in real time and makes suggestions to improve comfort. For example, it automatically adjusts air conditioner settings when the temperature is high. It also builds a system that makes suggestions to improve employee comfort based on environmental data. For example, it automatically operates a humidifier when humidity is low. It also collects environmental data in real time and makes suggestions to improve comfort. For example, it adjusts lighting brightness to reduce eye strain. This makes it possible to propose the optimal layout according to the organization's needs.

[0033] The simulation unit can virtually change the layout and simulate the effects. The simulation unit, for example, builds a system that visualizes the simulation results in real time and provides instant feedback. For example, it displays the simulation results in graphs and charts. The generative AI also visualizes the simulation results in real time and adjusts the layout based on the feedback. For example, it proposes an optimal layout based on the simulation results. It also visualizes the simulation results in real time and provides instant feedback. For example, it identifies areas for improvement based on the simulation results and makes suggestions. This allows the effects of layout changes to be confirmed in advance.

[0034] The prediction unit can use big data analysis and machine learning to build a predictive model that will be useful for future zoning plans and resource management. The prediction unit, for example, uses an emotion estimation function to predict the emotional state of employees and build a system that performs emotion-based resource management. For example, it makes suggestions to increase resources during periods of high stress. It also uses machine learning to analyze employee emotion data and perform emotion-based resource management. For example, it makes suggestions to place relaxation spaces in areas with low emotion scores. It also uses the emotion estimation function to predict the emotional state of employees and perform emotion-based resource management. For example, it proposes an optimal work schedule based on the emotion data. This makes it possible to make predictions that will be useful for future zoning plans and resource management.

[0035] The tracking unit can record the usage history of each room in detail and analyze usage patterns during specific time periods or events. For example, the tracking unit records the usage history of each room in detail and analyzes usage patterns during specific time periods or events. For example, the tracking unit stores the frequency and usage time of conference rooms in a database and identifies peak times. It also records the usage history of rooms for each event or project and analyzes usage patterns. For example, it stores room usage trends according to the progress of a project in a database. It also analyzes usage patterns during specific time periods or events based on the usage history of each room and proposes optimal room layouts and usage methods. For example, it adjusts the reservation system to match times when conference rooms are used most frequently. This makes it possible to understand usage patterns and propose optimal room layouts and usage methods.

[0036] The simulation unit visualizes the simulation results in real time and allows for instant feedback. The simulation unit, for example, builds a system that visualizes the simulation results in real time and allows for instant feedback. For example, it displays the simulation results in graphs and charts. The generative AI also visualizes the simulation results in real time and adjusts the layout based on the feedback. For example, it proposes an optimal layout based on the simulation results. The simulation results are also visualized in real time and allows for instant feedback. For example, it identifies and proposes areas for improvement based on the simulation results. This allows for instant confirmation of the simulation results and identification of areas for improvement.

[0037] The system can be used not only for offices, but also for proposing rental properties to individuals in the real estate industry. For example, the system's generation AI analyzes an individual's needs and desired conditions and then proposes the most suitable property based on that. For example, it proposes properties based on the desired floor plan and budget. The generation AI can also analyze an individual's desired conditions and propose the most suitable property. For example, it proposes properties based on the desired area and facilities. It can also analyze an individual's needs and desired conditions in detail and propose the most suitable property. For example, it proposes properties that take into account commuting time and the surrounding environment. This means that the system can be used not only for office management, but also for proposing rental properties in the real estate industry.

[0038] The system uses generative AI to analyze an individual's needs and desired conditions, and can suggest the most suitable property based on that. For example, the system can build a system that analyzes an individual's needs and desired conditions in detail and suggests the most suitable property. For example, it can suggest properties based on the desired floor plan and budget. The generative AI can also analyze an individual's desired conditions and suggest the most suitable property. For example, it can suggest properties based on the desired area and facilities. The system can also analyze an individual's needs and desired conditions in detail and suggest the most suitable property. For example, it can suggest properties that take into account commuting time and the surrounding environment. This makes it possible to suggest the most suitable property according to an individual's needs.

[0039] The system can predict future demand based on the usage history of rental properties and formulate optimal rental strategies. For example, the system analyzes the usage history of rental properties and builds a system that predicts future demand. For example, it identifies periods of high demand based on past data and proposes optimal rental strategies. It also uses machine learning to analyze the usage history of rental properties and predict future demand. For example, it identifies areas and property types with high demand and proposes optimal rental strategies. It also predicts future demand based on the usage history of rental properties and formulates optimal rental strategies. For example, it proposes adjusting rents during periods of high demand. This makes it possible to predict future demand and formulate optimal rental strategies.

[0040] The system can analyze environmental data of rental properties and suggest comfortable living environments. For example, the system can analyze environmental data of rental properties and build a system that suggests comfortable living environments. For example, it can suggest the most suitable property based on noise levels and sunlight conditions. In addition, the generation AI can analyze environmental data of rental properties and suggest comfortable living environments. For example, it can suggest properties in areas with low noise or good sunlight. In addition, it can suggest comfortable living environments based on environmental data of rental properties. For example, it can suggest properties that take into account surrounding noise levels and sunlight conditions. This makes it possible to suggest comfortable living environments.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The office management support system can further include a health management section. The health management section collects employee health data and monitors their health status. For example, it records employees' steps and heart rates and evaluates their health status. It also makes suggestions for maintaining health based on the health data. For example, it suggests an appropriate exercise program to employees who are not getting enough exercise. It also plans health promotion activities within the office based on the health data. For example, it holds regular health seminars and fitness events. This supports employee health management and helps create a healthy work environment.

[0043] The database unit can further collect energy management data and optimize energy consumption in the office. For example, it can record the power consumption and air conditioning usage in each room and evaluate energy efficiency. It can also make suggestions for energy conservation based on the energy consumption data. For example, it can automatically turn off lights in rooms that are used less frequently. It can also optimize energy management for the entire office based on the energy consumption data. For example, it can propose a schedule to reduce power usage during peak hours. This allows for efficient management of energy consumption in the office, contributing to cost reduction and environmental protection.

[0044] The tracking unit can also be equipped with security monitoring functions. For example, it can record detailed records of entry and exit from each room to detect unauthorized access. It can also analyze security camera footage in real time to detect abnormal behavior. For example, it can issue an alert if a suspicious person is seen wandering around the office late at night. It can also make suggestions for strengthening office security measures based on security data. For example, it can set access restrictions for specific areas. This can strengthen office security and create a safer working environment.

[0045] The simulation unit can also be equipped with an environmental simulation function. For example, it can virtually change the temperature, humidity, and lighting conditions in the office and simulate the effects. It can also suggest optimal environmental settings based on the results of the environmental simulation. For example, it can automatically adjust the air conditioner settings if the temperature is high. It can also make suggestions to improve employee comfort based on the results of the environmental simulation. For example, it can automatically operate a humidifier if the humidity is low. This allows the office environment to be optimized and employee comfort to be improved.

[0046] The database unit can also collect plant management data within the office and use it to improve the office environment. For example, it can record the type and location of plants in each room and monitor the health of the plants. It can also suggest optimal plant placement based on the plant management data. For example, it can adjust plant placement based on the amount of light and humidity. It can also suggest ways to improve air quality within the office based on the plant management data. For example, it can place plants with high air-purifying properties in specific areas. This can improve the office environment and increase employee comfort.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The database creation unit creates a database of office information. For example, it collects information such as the area of ​​each room, equipment, and usage status, and stores it in the database. Step 2: The tracking unit tracks usage in real time based on the office information compiled into a database by the database unit. For example, sensors are used to monitor the usage of each room, and the generated AI analyzes that data. Step 3: The proposal unit proposes partitions based on the organization's needs based on the usage tracked by the tracking unit. For example, the generation AI analyzes information such as the organization's business operations, number of employees, and necessary equipment, and then proposes the optimal layout based on that information. Step 4: The simulation unit simulates the section proposed by the proposal unit, for example, by virtually changing the layout and simulating the effect of the change. Step 5: The prediction unit predicts future division planning and resource management based on the simulation results obtained by the simulation unit. For example, it uses big data analysis and machine learning to build a predictive model that will be useful for future division planning and resource management.

[0049] (Example 2) The office management support system according to an embodiment of the present invention is a system that stores office information in a database, tracks usage in real time, proposes and simulates zoning based on organizational needs, and utilizes predictive models based on big data analysis and machine learning. As a result, the office management support system can improve the efficiency and productivity of office management.

[0050] An office management support system according to an embodiment includes a database creation unit, a tracking unit, a proposal unit, a simulation unit, and a prediction unit. The database creation unit creates a database of office information. For example, it collects information such as the area, facilities, and usage status of each room and stores it in a database. The tracking unit tracks usage status in real time based on the office information created by the database creation unit. For example, it monitors the usage status of each room using sensors, and a generation AI analyzes the data. The proposal unit proposes a layout that meets the needs of the organization based on the usage status tracked by the tracking unit. For example, the generation AI analyzes information such as the organization's business content, number of employees, and necessary facilities, and proposes an optimal layout based on that information. The simulation unit simulates the layout proposed by the proposal unit. For example, it virtually changes the layout and simulates the effects. The prediction unit predicts future layout planning and resource management based on the simulation results obtained by the simulation unit. For example, it uses big data analysis and machine learning to build a prediction model that is useful for future layout planning and resource management. As a result, the office management support system according to an embodiment can achieve improved efficiency and productivity in office management.

[0051] The database creation unit can collect information on the area, facilities, and usage status of each room in the office and store it in a database. For example, when collecting usage status and facility information for each room in the office, the database creation unit analyzes employees' emotional states in real time and adds satisfaction and stress levels to the database. For example, it analyzes employees' facial expressions and voices to calculate an emotional score. When collecting the usage history of each room, it also records employees' emotional data and analyzes emotional patterns during specific time periods or events. For example, it stores stress levels during meetings and satisfaction during breaks in the database. When collecting environmental data (temperature, humidity, lighting, etc.) in the office, it analyzes employees' emotional states and makes suggestions to improve comfort. For example, it records stress levels when the temperature is high and suggests optimal temperature settings. This allows for centralized management of information within the office.

[0052] The tracking unit uses sensors to monitor the usage of each room, and the generated AI can analyze that data. For example, the tracking unit records the usage history of each room in detail and analyzes usage patterns during specific time periods or events. For example, it stores the frequency and usage time of conference rooms in a database and identifies peak times. It also records the usage history of rooms for each event or project and analyzes usage patterns. For example, it stores room usage trends according to the progress of a project in a database. It also analyzes usage patterns during specific time periods or events based on the usage history of each room and proposes optimal room layouts and usage methods. For example, it adjusts the reservation system to match times when conference rooms are most frequently used. This makes it possible to understand office usage in real time.

[0053] The proposal department uses generative AI to analyze information on an organization's business operations, number of employees, and necessary equipment, and can propose the optimal layout based on that information. For example, the proposal department collects environmental data within the office (temperature, humidity, lighting, etc.) in real time and makes suggestions to improve comfort. For example, it automatically adjusts air conditioner settings when the temperature is high. It also builds a system that makes suggestions to improve employee comfort based on environmental data. For example, it automatically operates a humidifier when humidity is low. It also collects environmental data in real time and makes suggestions to improve comfort. For example, it adjusts lighting brightness to reduce eye strain. This makes it possible to propose the optimal layout according to the organization's needs.

[0054] The simulation unit can virtually change the layout and simulate the effects. The simulation unit, for example, builds a system that visualizes the simulation results in real time and provides instant feedback. For example, it displays the simulation results in graphs and charts. The generative AI also visualizes the simulation results in real time and adjusts the layout based on the feedback. For example, it proposes an optimal layout based on the simulation results. It also visualizes the simulation results in real time and provides instant feedback. For example, it identifies areas for improvement based on the simulation results and makes suggestions. This allows the effects of layout changes to be confirmed in advance.

[0055] The prediction unit can use big data analysis and machine learning to build a predictive model that will be useful for future zoning plans and resource management. The prediction unit, for example, uses an emotion estimation function to predict the emotional state of employees and build a system that performs emotion-based resource management. For example, it makes suggestions to increase resources during periods of high stress. It also uses machine learning to analyze employee emotion data and perform emotion-based resource management. For example, it makes suggestions to place relaxation spaces in areas with low emotion scores. It also uses the emotion estimation function to predict the emotional state of employees and perform emotion-based resource management. For example, it proposes an optimal work schedule based on the emotion data. This makes it possible to make predictions that will be useful for future zoning plans and resource management.

[0056] The database creation unit can use generative AI to perform emotion analysis and add employee satisfaction and stress levels to the database. For example, when collecting information on the usage status and equipment of each room in the office, the database creation unit analyzes employees' emotional states in real time and adds their satisfaction and stress levels to the database. For example, it analyzes employees' facial expressions and voices to calculate an emotion score. When collecting the usage history of each room, it also records employee emotional data and analyzes emotional patterns during specific time periods and events. For example, it stores stress levels during meetings and satisfaction during breaks in the database. When collecting environmental data in the office (temperature, humidity, lighting, etc.), it analyzes employees' emotional states and makes suggestions to improve comfort. For example, it records stress levels when the temperature is high and suggests optimal temperature settings. This allows the system to understand employees' emotional states and help improve the office environment.

[0057] The tracking unit can record the usage history of each room in detail and analyze usage patterns during specific time periods or events. For example, the tracking unit records the usage history of each room in detail and analyzes usage patterns during specific time periods or events. For example, the tracking unit stores the frequency and usage time of conference rooms in a database and identifies peak times. It also records the usage history of rooms for each event or project and analyzes usage patterns. For example, it stores room usage trends according to the progress of a project in a database. It also analyzes usage patterns during specific time periods or events based on the usage history of each room and proposes optimal room layouts and usage methods. For example, it adjusts the reservation system to match times when conference rooms are used most frequently. This makes it possible to understand usage patterns and propose optimal room layouts and usage methods.

[0058] The proposal unit can use the generative AI to analyze employee emotional data and propose the optimal layout based on that. The proposal unit, for example, uses an emotion estimation function to collect employee emotional data and use it to improve the office environment. For example, it analyzes employees' facial expressions and voices to identify their stress levels. It also builds a system that proposes improvement measures for the office environment based on the emotional data. For example, it installs relaxation spaces in areas with high stress. It also uses the emotion estimation function to collect employee emotional data and use it to improve the office environment. For example, it adjusts lighting and temperature based on the emotional data. This makes it possible to propose the optimal layout that takes into account the emotional state of employees.

[0059] The simulation unit visualizes the simulation results in real time and allows for instant feedback. The simulation unit, for example, builds a system that visualizes the simulation results in real time and allows for instant feedback. For example, it displays the simulation results in graphs and charts. The generative AI also visualizes the simulation results in real time and adjusts the layout based on the feedback. For example, it proposes an optimal layout based on the simulation results. The simulation results are also visualized in real time and allows for instant feedback. For example, it identifies and proposes areas for improvement based on the simulation results. This allows for instant confirmation of the simulation results and identification of areas for improvement.

[0060] The prediction unit can predict the emotional state of employees using the emotion estimation function and perform emotion-based resource management. The prediction unit, for example, uses the emotion estimation function to predict the emotional state of employees and builds a system that performs emotion-based resource management. For example, it makes a suggestion to increase resources during periods of high stress. In addition, it uses machine learning to analyze employee emotion data and perform emotion-based resource management. For example, it makes a suggestion to place relaxation spaces in areas with low emotion scores. In addition, it uses the emotion estimation function to predict the emotional state of employees and perform emotion-based resource management. For example, it proposes an optimal work schedule based on the emotion data. This makes it possible to manage resources taking into account the emotional state of employees.

[0061] The system can be used not only for offices, but also for proposing rental properties to individuals in the real estate industry. For example, the system's generation AI analyzes an individual's needs and desired conditions and then proposes the most suitable property based on that. For example, it proposes properties based on the desired floor plan and budget. The generation AI can also analyze an individual's desired conditions and propose the most suitable property. For example, it proposes properties based on the desired area and facilities. It can also analyze an individual's needs and desired conditions in detail and propose the most suitable property. For example, it proposes properties that take into account commuting time and the surrounding environment. This means that the system can be used not only for office management, but also for proposing rental properties in the real estate industry.

[0062] The system uses generative AI to analyze an individual's needs and desired conditions, and can suggest the most suitable property based on that. For example, the system can build a system that analyzes an individual's needs and desired conditions in detail and suggests the most suitable property. For example, it can suggest properties based on the desired floor plan and budget. The generative AI can also analyze an individual's desired conditions and suggest the most suitable property. For example, it can suggest properties based on the desired area and facilities. The system can also analyze an individual's needs and desired conditions in detail and suggest the most suitable property. For example, it can suggest properties that take into account commuting time and the surrounding environment. This makes it possible to suggest the most suitable property according to an individual's needs.

[0063] The system can predict future demand based on the usage history of rental properties and formulate optimal rental strategies. For example, the system analyzes the usage history of rental properties and builds a system that predicts future demand. For example, it identifies periods of high demand based on past data and proposes optimal rental strategies. It also uses machine learning to analyze the usage history of rental properties and predict future demand. For example, it identifies areas and property types with high demand and proposes optimal rental strategies. It also predicts future demand based on the usage history of rental properties and formulates optimal rental strategies. For example, it proposes adjusting rents during periods of high demand. This makes it possible to predict future demand and formulate optimal rental strategies.

[0064] The system can analyze environmental data of rental properties and suggest comfortable living environments. For example, the system can analyze environmental data of rental properties and build a system that suggests comfortable living environments. For example, it can suggest the most suitable property based on noise levels and sunlight conditions. In addition, the generation AI can analyze environmental data of rental properties and suggest comfortable living environments. For example, it can suggest properties in areas with low noise or good sunlight. In addition, it can suggest comfortable living environments based on environmental data of rental properties. For example, it can suggest properties that take into account surrounding noise levels and sunlight conditions. This makes it possible to suggest comfortable living environments.

[0065] The system can use the emotion estimation function to make rental proposals that take into account the emotional state of the individual. For example, the system uses the emotion estimation function to build a system that makes rental proposals that take into account the emotional state of the individual. For example, it can propose properties that are less stressful. Furthermore, based on the individual's emotional data, the generation AI makes optimal rental proposals. For example, it can prioritize proposals for properties with high emotional scores. Furthermore, the emotion estimation function can be used to make rental proposals that take into account the individual's emotional state. For example, it can propose the optimal living environment based on the emotional data. This makes it possible to make rental proposals that take into account the individual's emotional state.

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

[0067] The office management support system can further include a health management section. The health management section collects employee health data and monitors their health status. For example, it records employees' steps and heart rates and evaluates their health status. It also makes suggestions for maintaining health based on the health data. For example, it suggests an appropriate exercise program to employees who are not getting enough exercise. It also plans health promotion activities within the office based on the health data. For example, it holds regular health seminars and fitness events. This supports employee health management and helps create a healthy work environment.

[0068] The database unit can further collect energy management data and optimize energy consumption in the office. For example, it can record the power consumption and air conditioning usage in each room and evaluate energy efficiency. It can also make suggestions for energy conservation based on the energy consumption data. For example, it can automatically turn off lights in rooms that are used less frequently. It can also optimize energy management for the entire office based on the energy consumption data. For example, it can propose a schedule to reduce power usage during peak hours. This allows for efficient management of energy consumption in the office, contributing to cost reduction and environmental protection.

[0069] The tracking unit can also be equipped with security monitoring functions. For example, it can record detailed records of entry and exit from each room to detect unauthorized access. It can also analyze security camera footage in real time to detect abnormal behavior. For example, it can issue an alert if a suspicious person is seen wandering around the office late at night. It can also make suggestions for strengthening office security measures based on security data. For example, it can set access restrictions for specific areas. This can strengthen office security and create a safer working environment.

[0070] The proposal department can also make suggestions for reducing stress based on employee emotional data. For example, it can analyze employee emotional data and identify areas where stress is high. It can also suggest environmental improvements to reduce stress. For example, it can install relaxation spaces in areas where stress is high. It can also monitor employee stress levels in real time based on the emotional data and take appropriate measures. For example, it can provide counseling opportunities for employees with high stress levels. This can reduce employee stress and create a comfortable working environment.

[0071] The simulation unit can also be equipped with an environmental simulation function. For example, it can virtually change the temperature, humidity, and lighting conditions in the office and simulate the effects. It can also suggest optimal environmental settings based on the results of the environmental simulation. For example, it can automatically adjust the air conditioner settings if the temperature is high. It can also make suggestions to improve employee comfort based on the results of the environmental simulation. For example, it can automatically operate a humidifier if the humidity is low. This allows the office environment to be optimized and employee comfort to be improved.

[0072] The prediction unit can further perform emotion-based resource management based on employee emotional data. For example, it can analyze employee emotional data and propose resource allocation based on emotions. For example, it can propose increasing resources during periods of high stress. It can also predict employee emotional states based on the emotional data and perform emotion-based resource management. For example, it can propose locating relaxation spaces in areas with low emotional scores. It can also propose optimal work schedules based on the emotional data. This enables resource management that takes into account the emotional states of employees.

[0073] The database unit can also collect plant management data within the office and use it to improve the office environment. For example, it can record the type and location of plants in each room and monitor the health of the plants. It can also suggest optimal plant placement based on the plant management data. For example, it can adjust plant placement based on the amount of light and humidity. It can also suggest ways to improve air quality within the office based on the plant management data. For example, it can place plants with high air-purifying properties in specific areas. This can improve the office environment and increase employee comfort.

[0074] The tracking unit can further analyze emotion-based usage patterns based on employee emotional data. For example, it can analyze employee emotional data and identify emotional patterns during specific times or events. It can also suggest optimal room layouts and usage methods based on the emotional data. For example, it can suggest using relaxation spaces during times of high stress. It can also monitor employees' emotional states in real time based on the emotional data and take appropriate measures. For example, it can place relaxation spaces in areas with low emotional scores. This makes it possible to suggest optimal room layouts and usage methods that take employees' emotional states into account.

[0075] The proposal unit can further propose layouts based on emotions, based on employee emotional data. For example, it analyzes employee emotional data and proposes an optimal layout based on emotions. For example, it installs relaxation spaces in areas where stress is high. It also monitors employees' emotional states in real time based on the emotional data and takes appropriate measures. For example, it places relaxation spaces in areas with low emotional scores. It also proposes an optimal layout that takes employees' emotional states into account, based on the emotional data. For example, it adjusts lighting and temperature based on the emotional data. This makes it possible to propose an optimal layout that takes employees' emotional states into account.

[0076] The simulation unit can also perform emotion-based simulations based on employee emotional data. For example, it analyzes employee emotional data and performs emotion-based simulations. For example, it changes the layout of areas with high stress and simulates the effect. It also monitors employees' emotional states in real time based on the emotional data and takes appropriate measures. For example, it places relaxation spaces in areas with low emotional scores. It also performs simulations that take employees' emotional states into account based on the emotional data. For example, it adjusts lighting and temperature based on the emotional data. This makes it possible to perform simulations that take employees' emotional states into account.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The database creation unit creates a database of office information. For example, it collects information such as the area of ​​each room, equipment, and usage status, and stores it in the database. Step 2: The tracking unit tracks usage in real time based on the office information compiled into a database by the database unit. For example, sensors are used to monitor the usage of each room, and the generated AI analyzes that data. Step 3: The proposal unit proposes partitions based on the organization's needs based on the usage tracked by the tracking unit. For example, the generation AI analyzes information such as the organization's business operations, number of employees, and necessary equipment, and then proposes the optimal layout based on that information. Step 4: The simulation unit simulates the section proposed by the proposal unit, for example, by virtually changing the layout and simulating the effect of the change. Step 5: The prediction unit predicts future division planning and resource management based on the simulation results obtained by the simulation unit. For example, it uses big data analysis and machine learning to build a predictive model that will be useful for future division planning and resource management.

[0079] 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.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[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 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.

[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 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.

[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 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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 database creation unit that creates a database of office information; a tracking unit that tracks the usage status in real time based on the office information databased by the database creation unit; a proposal unit that proposes partitions according to the needs of the organization based on the usage status tracked by the tracking unit; a simulation unit that performs a simulation of the section proposed by the proposal unit; a prediction unit that predicts future partition plans and resource management based on the simulation results obtained by the simulation unit. A system characterized by:

2. The database creation unit Collect information on the area, facilities, and usage status of each office room and store it in a database 2. The system of claim 1.

3. The tracking unit Sensors are used to monitor the usage of each room, and the data is analyzed by AI.

2. The system of claim 1.

4. The proposal unit Using generative AI, the company analyzes the organization's business operations, number of employees, and necessary equipment, and then proposes the optimal layout based on that information.

2. The system of claim 1.

5. The simulation unit Virtually change the layout and simulate the effect 2. The system of claim 1.

6. The prediction unit Use big data analytics and machine learning to build predictive models that will help with future plot planning and resource management 2. The system of claim 1.

Images