system

Abstract

The system according to the embodiment aims to grasp the usage status of office space in real time and propose an optimal layout. [Solution] A system according to an embodiment includes a database creation unit, a tracking unit, a proposal unit, and a simulation unit. The database creation unit creates a database of information about each space in an office. The tracking unit tracks the usage status of each space in real time based on the information databased by the database creation unit. The proposal unit proposes partitions based on the needs of the organization based on the data collected by the tracking unit. The simulation unit simulates the layout proposed by the proposal unit.

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 technology has the problem that it is difficult to grasp the usage status of office space in real time and propose the optimal layout.

[0005] The system according to the embodiment aims to grasp the usage status of office space in real time and propose an optimal layout. [Means for solving the problem]

[0006] The system according to the embodiment includes a database unit, a tracking unit, a proposal unit, and a simulation unit. The database unit creates a database of information about each space in the office. The tracking unit tracks the usage status of each space in real time based on the information databased by the database unit. The proposal unit proposes partitions according to the needs of the organization based on the data collected by the tracking unit. The simulation unit simulates the layout proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the usage status of office space in real time and propose the optimal layout. [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) An office management support system according to an embodiment of the present invention stores information about each office space in a database, tracks usage in real time, and proposes and simulates space allocations tailored to the organization's needs. This system is designed to support the improvement of office efficiency and productivity. For example, information about each office space is stored in a database, including information about each space's area, facilities, and usage. Next, sensors and IoT devices are used to track the usage of each space in real time. For example, the system can grasp the usage status of conference rooms and desk occupancy in real time. Next, based on the collected data, space allocation proposals and simulations tailored to the organization's needs are performed. For example, optimal layouts can be proposed based on the number of people and work content of specific departments. Simulations can also be performed to confirm the effects of layout changes in advance. Furthermore, big data analysis and machine learning are used to perform future space planning and resource management. For example, future space demand can be predicted based on past usage data, and appropriate resource allocation can be performed. Machine learning can also be used to analyze usage patterns and propose efficient space utilization methods. This system not only contributes to the improvement of office efficiency and productivity, but can also be used in the real estate industry to propose rental properties to individuals. For example, it can propose rental properties that are optimally suited to an individual's lifestyle and needs. This improves the efficiency of rental property utilization and increases customer satisfaction. As a result, the office management support system can support the improvement of efficiency and productivity in office management by storing information on each office space in a database, tracking usage in real time, and proposing and simulating divisions according to the organization's needs.

[0029] An office management support system according to an embodiment includes a database creation unit, a tracking unit, a proposal unit, and a simulation unit. The database creation unit creates a database of information about each office space. The information about each office space includes, but is not limited to, information about floor area, facilities, and usage status. The database creation unit creates a database of information about each space using, for example, cloud storage. The cloud storage is used to streamline data storage and access methods. The tracking unit tracks the usage status of each space in real time based on the information created in the database by the database creation unit. The tracking unit tracks the usage status of each space in real time using, for example, a temperature sensor, a motion sensor, and a pressure sensor for determining desk occupancy. The temperature sensor measures the temperature of each space, and the motion sensor detects the presence of a person in the space. The pressure sensor is used to determine desk occupancy. The proposal unit proposes partitions based on the data collected by the tracking unit in accordance with the needs of the organization. The proposal unit predicts future space demand based on, for example, past usage data, and allocates resources appropriately. The proposal unit can also use machine learning to analyze usage patterns and propose efficient space utilization methods. The simulation unit simulates the layout proposed by the proposal unit. The simulation unit, for example, performs a simulation to confirm the effects of layout changes in advance. The simulation unit is used to evaluate the effects of layout changes and propose an optimal layout. As a result, the office management support system according to the embodiment can support the improvement of efficiency and productivity in office management by storing information on each space in a database, tracking usage status in real time, and proposing and simulating partitions according to the needs of the organization.

[0030] The tracking unit can track the usage status of each space in real time using a temperature sensor, a motion sensor, or a pressure sensor for determining the occupancy status of a desk. The temperature sensor is used, for example, to measure the temperature of each space. The temperature sensor is installed taking into consideration the sensor's accuracy, installation location, measurement range, etc. For example, a temperature sensor is installed in each office space to collect temperature data in real time. The motion sensor is used, for example, to detect the presence of a person in a space. The motion sensor is installed taking into consideration the sensor's sensitivity, installation location, detection range, etc. For example, a motion sensor is installed in a conference room or desk area to detect the presence of a person in real time. The pressure sensor is used, for example, to determine the occupancy status of a desk. The pressure sensor is installed taking into consideration the sensor's accuracy, installation location, measurement range, etc. For example, a pressure sensor is installed under a desk chair or desk to determine the desk occupancy status in real time. As a result, by using the temperature sensor, motion sensor, and pressure sensor, the usage status of each space can be accurately determined in real time. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or without AI. For example, the tracking unit can input data obtained from a sensor into the generation AI, which can then analyze the data to understand usage status.

[0031] The database creation unit can use cloud storage to create a database of information for each space. Cloud storage, for example, includes functions for efficiently storing information for each space and making it easy to access. Cloud storage is selected taking into consideration the cloud service used, the data storage method, the access method, and the like. For example, cloud storage has data backup and recovery functions, ensuring data security. Cloud storage also provides data scalability, allowing data capacity to be expanded as needed. As a result, by using cloud storage, information for each space can be efficiently created as a database and made easy to access. Some or all of the above-described processing in the database creation unit may be performed, for example, using AI, or may be performed without AI. For example, the database creation unit can input data stored in cloud storage into a generation AI, which then analyzes the data and creates a database.

[0032] The proposal unit can predict future space demand and allocate resources based on past usage data. The past usage data includes, for example, the usage status of each space, user attribute information, and usage frequency. The past usage data is collected taking into account the type of data, collection period, collection method, and the like. For example, the past usage data is automatically collected using sensors or IoT devices. The proposal unit uses an algorithm to predict future space demand based on the past usage data. For example, the proposal unit can analyze the past usage data using a machine learning algorithm to predict future space demand. The proposal unit allocates resources based on the predicted space demand. For example, the proposal unit can optimally allocate resources to each space according to future space demand. This allows for appropriate resource allocation and efficient space utilization by predicting future space demand based on the past usage data. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the proposal unit can input past usage data into a generation AI, which can analyze the data and predict future space demand.

[0033] The simulation unit can perform a simulation to confirm the effect of a layout change in advance. The simulation unit, for example, uses a simulation algorithm for evaluating the effect of the layout change. The simulation unit performs the simulation taking into consideration the algorithm to be used, the scope of the simulation, the evaluation criteria, etc. For example, the simulation unit constructs a simulation model and executes a simulation to evaluate the effect of the layout change. The simulation unit can evaluate the effect of the layout change based on the simulation results and propose an optimal layout. This makes it possible to propose an optimal layout and achieve efficient space utilization by confirming the effect of the layout change in advance. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input a simulation of the layout change to a generation AI, which then executes the simulation to evaluate the effect.

[0034] The suggestion unit can use machine learning to analyze usage patterns and propose efficient space utilization methods. Machine learning includes algorithms such as random forests and support vector machines. The suggestion unit performs machine learning taking into account the algorithm to be used, training data, evaluation methods, etc. For example, the suggestion unit can use random forests to analyze past usage data and extract usage patterns. The suggestion unit can also use support vector machines to classify usage patterns and propose efficient space utilization methods. In this way, machine learning can be used to analyze usage patterns and propose efficient space utilization methods. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past usage data into a generation AI, which analyzes the data, extracts usage patterns, and proposes efficient space utilization methods.

[0035] The suggestion unit can make a partition proposal using an algorithm such as a random forest or a support vector machine. A random forest is, for example, an algorithm that performs classification and regression using multiple decision trees. The suggestion unit can analyze past usage data using a random forest and make an optimal partition proposal. A support vector machine is, for example, an algorithm that performs classification and regression in a high-dimensional space. The suggestion unit can analyze usage data using a support vector machine and make an optimal partition proposal. As a result, optimal partition proposals can be made using algorithms such as a random forest or a support vector machine. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make optimal partition proposals by having a generation AI execute a random forest or support vector machine algorithm.

[0036] The database creation unit can adjust the level of detail of information based on the frequency of use of each space during database creation. The frequency of use includes, for example, the number of times each space is used, the duration of use, and the number of users. The frequency of use is measured taking into account the type of data, collection period, collection method, and the like. For example, the frequency of use is automatically collected using sensors or IoT devices. The database creation unit uses an algorithm to adjust the level of detail of information based on the frequency of use. For example, the database creation unit can create a detailed database of information about frequently used conference rooms, including reservation status and facility information. Information about less frequently used spaces can be simplified, and only basic information can be created in the database. This enables efficient database creation by adjusting the level of detail of information based on the frequency of use of each space. Some or all of the above-described processing in the database creation unit may be performed using, or without, AI. For example, the database creation unit can input usage frequency data into a generation AI, which analyzes the data and adjusts the level of detail of the information.

[0037] The database creation unit can include the physical characteristics of each space when creating the database. Examples of physical characteristics include lighting brightness, sound reverberation, and temperature. The physical characteristics are collected taking into consideration the type of data, measurement method, evaluation criteria, and the like. For example, the database creation unit can create a database of the lighting brightness and sound reverberation of a conference room, allowing users to select the optimal environment. A database of the lighting conditions and sound reverberation of a desk area can be created to provide an environment that is easy to concentrate. A database of the lighting and sound reverberation of a break space can be created to provide an environment that is easy to relax. By creating a database of the physical characteristics of each space, users can select the optimal environment. Some or all of the above-described processing in the database creation unit may be performed, for example, using AI, or may be performed without AI. For example, the database creation unit can input physical characteristic data into a generation AI, which then analyzes the data and creates a database.

[0038] The database creation unit can include attribute information of users of each space when creating a database. The attribute information includes, for example, age, occupation, and gender. The attribute information is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the database creation unit can create a database of the age range of users of each space to provide an appropriate environment. It can create a database of the occupations of users of each space to provide an environment according to their work content. It can create a database of the attribute information of users of each space and make customized suggestions. This makes it possible to create customized suggestions by creating a database of the attribute information of users of each space. Some or all of the above-mentioned processing in the database creation unit can be performed, for example, using AI, or can be performed without using AI. For example, the database creation unit can input user attribute information into a generation AI, which can analyze the data and create a database.

[0039] The database creation unit can include environmental data for each space when creating a database. The environmental data includes, for example, temperature, humidity, and lighting brightness. The environmental data is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the database creation unit can create a database of the temperature and humidity for each space to provide a comfortable environment. The environmental data for each space can be created in a database to allow the user to select the optimal environment. The temperature and humidity for each space can be created in a database to make suggestions in response to changes in the environment. In this way, by creating a database of the environmental data for each space, a comfortable environment can be provided. Some or all of the above-mentioned processing in the database creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the database creation unit can input the environmental data into a generation AI, which analyzes the data and creates a database.

[0040] During tracking, the tracking unit analyzes the user's movement lines in each space to grasp the usage status in detail. The movement lines include, for example, the user's movement route, stay time, congestion status, etc. The movement lines are analyzed taking into consideration the type of data, analysis method, evaluation criteria, etc. For example, the tracking unit can analyze the user's movement lines in each space to grasp the congestion status. The tracking unit can propose an efficient layout based on the movement line data. For example, the tracking unit can analyze the user's movement lines and provide an optimal movement line. As a result, by analyzing the user's movement lines in each space, an efficient layout can be proposed. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the movement line data to a generation AI, which can analyze the data to grasp the movement lines.

[0041] During tracking, the tracking unit measures the user's stay time in each space and can grasp the usage status in detail. The stay time includes, for example, the time the user stays in each space, the stay frequency, and the stay pattern. The stay time is measured taking into consideration the type of data, measurement method, evaluation criteria, etc. For example, the tracking unit can measure the user's stay time in each space and grasp the usage status. The tracking unit can suggest efficient space use based on the stay time data. For example, the tracking unit can analyze the user's stay time and provide the optimal stay time. In this way, by measuring the user's stay time in each space, efficient space use can be suggested. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or without AI. For example, the tracking unit can input the stay time data to a generation AI, which can analyze the data to grasp the stay time.

[0042] The tracking unit can use device information of users of each space during tracking. The device information includes, for example, smartphone location information, device type, and device usage status. The device information is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the tracking unit can use the location information of the smartphones of users of each space to grasp usage status in real time. The tracking unit can suggest efficient space usage based on the device information. For example, the tracking unit can analyze the user's device information and provide an optimal flow line. As a result, by using the device information of users of each space, usage status can be grasped in real time. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input device information to a generation AI, which can analyze the data and grasp usage status.

[0043] During tracking, the tracking unit can grasp the usage status based on the schedule information of the user of each space. The schedule information includes, for example, calendar information, the type of schedule, and the time of the schedule. The schedule information is collected taking into consideration the type of data, the collection method, the evaluation criteria, and the like. For example, the tracking unit can refer to the schedule information of the user of each space to grasp the usage status. The tracking unit can suggest efficient space usage based on the schedule information. For example, the tracking unit can analyze the user's schedule information and provide the optimal usage time. As a result, efficient space usage can be suggested by referring to the schedule information of the user of each space. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the schedule information to a generation AI, which can analyze the data to grasp the usage status.

[0044] When making a proposal, the suggestion unit can collect user feedback for each space and optimize the proposal content. The feedback includes, for example, user opinions, evaluations, and areas for improvement. The feedback is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the suggestion unit can collect user feedback for each space and optimize the proposal content. The suggestion unit can propose efficient space use based on the feedback data. For example, the suggestion unit can analyze user feedback and provide optimal suggestions. As a result, by collecting user feedback for each space, the proposal content can be optimized and efficient space use can be achieved. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input feedback data to a generation AI, which can analyze the data and optimize the proposal content.

[0045] When making a proposal, the suggestion unit can customize the proposal content based on the work content of the user of each space. The work content includes, for example, the type of work, the importance of the work, and the frequency of the work. The work content is collected taking into consideration the type of data, the collection method, the evaluation criteria, and the like. For example, the suggestion unit can make optimal proposals based on the work content of the user of each space. The suggestion unit can propose efficient space utilization based on the work content data. For example, the suggestion unit can analyze the user's work content and propose an optimal layout. This enables optimal proposals to be made by customizing the proposal content based on the work content of the user of each space. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input work content data into a generation AI, which can analyze the data and customize the proposal content.

[0046] When making a proposal, the suggestion unit can optimize the proposal content based on the user's past usage history of each space. The usage history includes, for example, the number of times each space is used, the usage time, and user feedback. The usage history is collected taking into consideration the type of data, collection period, collection method, etc. For example, the suggestion unit can refer to the user's past usage history of each space and make optimal suggestions. The suggestion unit can propose efficient space use based on the usage history data. For example, the suggestion unit can analyze the user's past usage history and propose an optimal layout. As a result, by referring to the user's past usage history of each space, the proposal content can be optimized and efficient space use can be achieved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input usage history data to a generation AI, which can analyze the data and optimize the proposal content.

[0047] When making a proposal, the suggestion unit can customize the proposal content based on the geographical location information of the user of each space. The geographical location information includes, for example, the user's current location, travel route, and place of stay. The geographical location information is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the suggestion unit can make optimal proposals by taking into consideration the geographical location information of the user of each space. The suggestion unit can propose efficient space utilization based on the geographical location information. For example, the suggestion unit can analyze the user's geographical location information and propose an optimal layout. This enables optimal proposals by taking into consideration the geographical location information of the user of each space. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input geographical location information to a generation AI, which can analyze the data and customize the proposal content.

[0048] During the simulation, the simulation unit analyzes user movement lines in each space and can confirm the effects of layout changes in detail. The movement lines include, for example, user movement routes, stay times, and congestion levels. The movement lines are analyzed taking into account the type of data, analysis method, evaluation criteria, and the like. For example, the simulation unit can analyze user movement lines in each space and confirm the effects of layout changes. The simulation unit can propose an efficient layout based on the movement line data. For example, the simulation unit can analyze user movement lines and provide optimal movement lines. As a result, an efficient layout can be proposed by analyzing user movement lines in each space. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input movement line data into a generation AI, which can analyze the data and understand the movement lines.

[0049] During the simulation, the simulation unit measures the user's stay time in each space and can confirm the effect of the layout change in detail. The stay time includes, for example, the time spent by the user in each space, the frequency of stay, and the stay pattern. The stay time is measured taking into account the type of data, measurement method, evaluation criteria, etc. For example, the simulation unit can measure the user's stay time in each space and confirm the effect of the layout change. The simulation unit can propose efficient space use based on the stay time data. For example, the simulation unit can analyze the user's stay time and provide an optimal stay time. As a result, by measuring the user's stay time in each space, efficient space use can be proposed. Some or all of the above-mentioned processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the stay time data into a generation AI, which can analyze the data and determine the stay time.

[0050] The simulation unit can use device information of users in each space during simulation. The device information includes, for example, smartphone location information, device type, and device usage status. The device information is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the simulation unit can use smartphone location information of users in each space to perform a simulation in real time. The simulation unit can propose efficient space usage based on the device information. For example, the simulation unit can analyze user device information and provide optimal traffic lines. This allows for real-time simulations by using the device information of users in each space. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input device information into a generation AI, which then analyzes the data and performs a simulation.

[0051] During the simulation, the simulation unit can confirm the effect of the layout change by referring to the schedule information of the users of each space. The schedule information includes, for example, calendar information, the type of schedule, and the time of the schedule. The schedule information is collected taking into consideration the type of data, the collection method, the evaluation criteria, and the like. For example, the simulation unit can confirm the effect of the layout change by referring to the schedule information of the users of each space. The simulation unit can propose efficient space utilization based on the schedule information. For example, the simulation unit can analyze the user's schedule information and provide an optimal layout. As a result, efficient space utilization can be proposed by referring to the user's schedule information of each space. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the schedule information to a generation AI, which can analyze the data and confirm the effect of the layout change.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The tracking unit collects health data from users of each space and can grasp usage status in detail. For example, it can collect data such as a user's heart rate, number of steps, and calorie consumption to monitor their health status. This makes it possible to suggest space usage based on their health status. For example, if the heart rate is high, it can suggest a space where you can relax, and if the number of steps is low, it can suggest a space where you can exercise.

[0054] The database creation unit can collect user preference data for each space and create a database. For example, it can collect preference data such as the user's favorite music, drinks, and comfortable chairs, and suggest the optimal space for the user. This makes it possible to suggest spaces customized according to the user's preferences. For example, it can suggest spaces that play the user's favorite music or that serve the user's favorite drinks.

[0055] The simulation unit collects energy consumption data from users in each space and can confirm the effects of layout changes in detail. For example, it can collect energy consumption data for lighting and air conditioning in each space and propose energy-efficient layouts. This enables efficient space use with minimal energy consumption. For example, it can propose lighting layouts that make use of natural light and efficient air conditioning layouts.

[0056] The database unit can collect communication data of users in each space and store it in a database. For example, it can collect communication data such as the content of conversations between users and email exchanges, and suggest spaces where efficient communication can take place. This facilitates communication and improves work efficiency. For example, it can suggest layouts that make conversations easy and spaces that maintain privacy.

[0057] The simulation unit collects productivity data for users in each space and can confirm the effects of layout changes in detail. For example, it can measure the work efficiency and concentration levels of users in each space and propose layouts that are expected to improve productivity. This improves work efficiency and enables more productive space usage. For example, it can propose desk arrangements that encourage concentration and meeting room layouts that facilitate smooth communication.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The database creation unit creates a database of information about each space in the office. Information about each space in the office includes, but is not limited to, information about the area, facilities, and usage status. The database creation unit creates a database of information about each space using, for example, cloud storage. Cloud storage is used to streamline data storage and access methods. Step 2: The tracking unit tracks the usage status of each space in real time based on the information compiled into a database by the database compilation unit. The tracking unit tracks the usage status of each space in real time using, for example, a temperature sensor, a motion sensor, and a pressure sensor for grasping the occupancy status of a desk. The temperature sensor measures the temperature of each space, and the motion sensor detects the presence of a person in the space. The pressure sensor is used to grasp the occupancy status of a desk. Step 3: The suggestion unit proposes partitions based on the organization's needs based on the data collected by the tracking unit. For example, the suggestion unit predicts future space demand based on past usage data and allocates resources appropriately. The suggestion unit can also use machine learning to analyze usage patterns and propose efficient space utilization methods. Step 4: The simulation unit simulates the layout proposed by the proposal unit. The simulation unit performs a simulation to confirm the effect of a layout change in advance, for example. The simulation unit is used to evaluate the effect of the layout change and propose an optimal layout.

[0060] (Example 2) An office management support system according to an embodiment of the present invention stores information about each office space in a database, tracks usage in real time, and proposes and simulates space allocations tailored to the organization's needs. This system is designed to support the improvement of office efficiency and productivity. For example, information about each office space is stored in a database, including information about each space's area, facilities, and usage. Next, sensors and IoT devices are used to track the usage of each space in real time. For example, the system can grasp the usage status of conference rooms and desk occupancy in real time. Next, based on the collected data, space allocation proposals and simulations tailored to the organization's needs are performed. For example, optimal layouts can be proposed based on the number of people and work content of specific departments. Simulations can also be performed to confirm the effects of layout changes in advance. Furthermore, big data analysis and machine learning are used to perform future space planning and resource management. For example, future space demand can be predicted based on past usage data, and appropriate resource allocation can be performed. Machine learning can also be used to analyze usage patterns and propose efficient space utilization methods. This system not only contributes to the improvement of office efficiency and productivity, but can also be used in the real estate industry to propose rental properties to individuals. For example, it can propose rental properties that are optimally suited to an individual's lifestyle and needs. This improves the efficiency of rental property utilization and increases customer satisfaction. As a result, the office management support system can support the improvement of efficiency and productivity in office management by storing information on each office space in a database, tracking usage in real time, and proposing and simulating divisions according to the organization's needs.

[0061] An office management support system according to an embodiment includes a database creation unit, a tracking unit, a proposal unit, and a simulation unit. The database creation unit creates a database of information about each office space. The information about each office space includes, but is not limited to, information about floor area, facilities, and usage status. The database creation unit creates a database of information about each space using, for example, cloud storage. The cloud storage is used to streamline data storage and access methods. The tracking unit tracks the usage status of each space in real time based on the information created in the database by the database creation unit. The tracking unit tracks the usage status of each space in real time using, for example, a temperature sensor, a motion sensor, and a pressure sensor for determining desk occupancy. The temperature sensor measures the temperature of each space, and the motion sensor detects the presence of a person in the space. The pressure sensor is used to determine desk occupancy. The proposal unit proposes partitions based on the data collected by the tracking unit in accordance with the needs of the organization. The proposal unit predicts future space demand based on, for example, past usage data, and allocates resources appropriately. The proposal unit can also use machine learning to analyze usage patterns and propose efficient space utilization methods. The simulation unit simulates the layout proposed by the proposal unit. The simulation unit, for example, performs a simulation to confirm the effects of layout changes in advance. The simulation unit is used to evaluate the effects of layout changes and propose an optimal layout. As a result, the office management support system according to the embodiment can support the improvement of efficiency and productivity in office management by storing information on each space in a database, tracking usage status in real time, and proposing and simulating partitions according to the needs of the organization.

[0062] The tracking unit can track the usage status of each space in real time using a temperature sensor, a motion sensor, or a pressure sensor for determining the occupancy status of a desk. The temperature sensor is used, for example, to measure the temperature of each space. The temperature sensor is installed taking into consideration the sensor's accuracy, installation location, measurement range, etc. For example, a temperature sensor is installed in each office space to collect temperature data in real time. The motion sensor is used, for example, to detect the presence of a person in a space. The motion sensor is installed taking into consideration the sensor's sensitivity, installation location, detection range, etc. For example, a motion sensor is installed in a conference room or desk area to detect the presence of a person in real time. The pressure sensor is used, for example, to determine the occupancy status of a desk. The pressure sensor is installed taking into consideration the sensor's accuracy, installation location, measurement range, etc. For example, a pressure sensor is installed under a desk chair or desk to determine the desk occupancy status in real time. As a result, by using the temperature sensor, motion sensor, and pressure sensor, the usage status of each space can be accurately determined in real time. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or without AI. For example, the tracking unit can input data obtained from a sensor into the generation AI, which can then analyze the data to understand usage status.

[0063] The database creation unit can use cloud storage to create a database of information for each space. Cloud storage, for example, includes functions for efficiently storing information for each space and making it easy to access. Cloud storage is selected taking into consideration the cloud service used, the data storage method, the access method, and the like. For example, cloud storage has data backup and recovery functions, ensuring data security. Cloud storage also provides data scalability, allowing data capacity to be expanded as needed. As a result, by using cloud storage, information for each space can be efficiently created as a database and made easy to access. Some or all of the above-described processing in the database creation unit may be performed, for example, using AI, or may be performed without AI. For example, the database creation unit can input data stored in cloud storage into a generation AI, which then analyzes the data and creates a database.

[0064] The proposal unit can predict future space demand and allocate resources based on past usage data. The past usage data includes, for example, the usage status of each space, user attribute information, and usage frequency. The past usage data is collected taking into account the type of data, collection period, collection method, and the like. For example, the past usage data is automatically collected using sensors or IoT devices. The proposal unit uses an algorithm to predict future space demand based on the past usage data. For example, the proposal unit can analyze the past usage data using a machine learning algorithm to predict future space demand. The proposal unit allocates resources based on the predicted space demand. For example, the proposal unit can optimally allocate resources to each space according to future space demand. This allows for appropriate resource allocation and efficient space utilization by predicting future space demand based on the past usage data. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the proposal unit can input past usage data into a generation AI, which can analyze the data and predict future space demand.

[0065] The simulation unit can perform a simulation to confirm the effect of a layout change in advance. The simulation unit, for example, uses a simulation algorithm for evaluating the effect of the layout change. The simulation unit performs the simulation taking into consideration the algorithm to be used, the scope of the simulation, the evaluation criteria, etc. For example, the simulation unit constructs a simulation model and executes a simulation to evaluate the effect of the layout change. The simulation unit can evaluate the effect of the layout change based on the simulation results and propose an optimal layout. This makes it possible to propose an optimal layout and achieve efficient space utilization by confirming the effect of the layout change in advance. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input a simulation of the layout change to a generation AI, which then executes the simulation to evaluate the effect.

[0066] The suggestion unit can use machine learning to analyze usage patterns and propose efficient space utilization methods. Machine learning includes algorithms such as random forests and support vector machines. The suggestion unit performs machine learning taking into account the algorithm to be used, training data, evaluation methods, etc. For example, the suggestion unit can use random forests to analyze past usage data and extract usage patterns. The suggestion unit can also use support vector machines to classify usage patterns and propose efficient space utilization methods. In this way, machine learning can be used to analyze usage patterns and propose efficient space utilization methods. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past usage data into a generation AI, which analyzes the data, extracts usage patterns, and proposes efficient space utilization methods.

[0067] The suggestion unit can make a partition proposal using an algorithm such as a random forest or a support vector machine. A random forest is, for example, an algorithm that performs classification and regression using multiple decision trees. The suggestion unit can analyze past usage data using a random forest and make an optimal partition proposal. A support vector machine is, for example, an algorithm that performs classification and regression in a high-dimensional space. The suggestion unit can analyze usage data using a support vector machine and make an optimal partition proposal. As a result, optimal partition proposals can be made using algorithms such as a random forest or a support vector machine. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make optimal partition proposals by having a generation AI execute a random forest or support vector machine algorithm.

[0068] The office management support system further includes a database creation unit that estimates a user's emotions and prioritizes the information to be stored in a database based on the estimated user emotions. The database creation unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. The emotion estimation algorithm can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the database creation unit can analyze the user's facial expression data and estimate whether the user is stressed or relaxed. The database creation unit prioritizes the information to be stored in a database based on the estimated user emotions. For example, if the user is stressed, important information is prioritized and stored in a database for quick access. If the user is relaxed, detailed information is also stored in a database for later reference. This enables database creation according to the user's needs by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the database creation unit may be performed using, for example, an AI, or without an AI. For example, the database creation unit can input the user's emotional data into the generation AI, which can then analyze the data and determine the priority of the information.

[0069] The database creation unit can adjust the level of detail of information based on the frequency of use of each space during database creation. The frequency of use includes, for example, the number of times each space is used, the duration of use, and the number of users. The frequency of use is measured taking into account the type of data, collection period, collection method, and the like. For example, the frequency of use is automatically collected using sensors or IoT devices. The database creation unit uses an algorithm to adjust the level of detail of information based on the frequency of use. For example, the database creation unit can create a detailed database of information about frequently used conference rooms, including reservation status and facility information. Information about less frequently used spaces can be simplified, and only basic information can be created in the database. This enables efficient database creation by adjusting the level of detail of information based on the frequency of use of each space. Some or all of the above-described processing in the database creation unit may be performed using, or without, AI. For example, the database creation unit can input usage frequency data into a generation AI, which analyzes the data and adjusts the level of detail of the information.

[0070] The database creation unit can include the physical characteristics of each space when creating the database. Examples of physical characteristics include lighting brightness, sound reverberation, and temperature. The physical characteristics are collected taking into consideration the type of data, measurement method, evaluation criteria, and the like. For example, the database creation unit can create a database of the lighting brightness and sound reverberation of a conference room, allowing users to select the optimal environment. A database of the lighting conditions and sound reverberation of a desk area can be created to provide an environment that is easy to concentrate. A database of the lighting and sound reverberation of a break space can be created to provide an environment that is easy to relax. By creating a database of the physical characteristics of each space, users can select the optimal environment. Some or all of the above-described processing in the database creation unit may be performed, for example, using AI, or may be performed without AI. For example, the database creation unit can input physical characteristic data into a generation AI, which then analyzes the data and creates a database.

[0071] The database creation unit can estimate the user's emotions and adjust the categories of information to be stored in the database based on the estimated user emotions. Examples of user emotions include stress, relaxation, and hurry. The user's emotions are estimated using an emotion estimation algorithm. For example, the database creation unit can analyze the user's facial expression data and estimate whether the user is stressed or relaxed. The database creation unit adjusts the categories of information to be stored in the database based on the estimated user emotions. For example, if the user is stressed, important categories are prioritized in the database for quick access. If the user is relaxed, detailed categories are also included in the database for later reference. This allows for adjusting the categories of information based on the user's emotions, enabling database creation according to the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the database creation unit may be performed using, for example, AI, or without AI. For example, the database creation unit can input the user's emotional data into the generation AI, which can then analyze the data and adjust the categories of information.

[0072] The database creation unit can include attribute information of users of each space when creating a database. The attribute information includes, for example, age, occupation, and gender. The attribute information is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the database creation unit can create a database of the age range of users of each space to provide an appropriate environment. It can create a database of the occupations of users of each space to provide an environment according to their work content. It can create a database of the attribute information of users of each space and make customized suggestions. This makes it possible to create customized suggestions by creating a database of the attribute information of users of each space. Some or all of the above-mentioned processing in the database creation unit can be performed, for example, using AI, or can be performed without using AI. For example, the database creation unit can input user attribute information into a generation AI, which can analyze the data and create a database.

[0073] The database creation unit can include environmental data for each space when creating a database. The environmental data includes, for example, temperature, humidity, and lighting brightness. The environmental data is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the database creation unit can create a database of the temperature and humidity for each space to provide a comfortable environment. The environmental data for each space can be created in a database to allow the user to select the optimal environment. The temperature and humidity for each space can be created in a database to make suggestions in response to changes in the environment. In this way, by creating a database of the environmental data for each space, a comfortable environment can be provided. Some or all of the above-mentioned processing in the database creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the database creation unit can input the environmental data into a generation AI, which analyzes the data and creates a database.

[0074] The tracking unit can estimate the user's emotions and prioritize the information to be tracked based on the estimated user emotions. Examples of user emotions include stress, relaxation, and hurry. The user's emotions are estimated using an emotion estimation algorithm. For example, the tracking unit can analyze the user's facial expression data to estimate whether the user is stressed or relaxed. The tracking unit prioritizes the information to be tracked based on the estimated user emotions. For example, if the user is stressed, important information is prioritized and provided quickly. If the user is relaxed, detailed information is tracked and made available for later reference. This enables tracking according to the user's needs by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the tracking unit can be performed using, for example, AI or without AI. For example, the tracking unit can input the user's emotional data into the generation AI, which can then analyze the data and determine the priority of the information.

[0075] During tracking, the tracking unit analyzes the user's movement lines in each space to grasp the usage status in detail. The movement lines include, for example, the user's movement route, stay time, congestion status, etc. The movement lines are analyzed taking into consideration the type of data, analysis method, evaluation criteria, etc. For example, the tracking unit can analyze the user's movement lines in each space to grasp the congestion status. The tracking unit can propose an efficient layout based on the movement line data. For example, the tracking unit can analyze the user's movement lines and provide an optimal movement line. As a result, by analyzing the user's movement lines in each space, an efficient layout can be proposed. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the movement line data to a generation AI, which can analyze the data to grasp the movement lines.

[0076] During tracking, the tracking unit measures the user's stay time in each space and can grasp the usage status in detail. The stay time includes, for example, the time the user stays in each space, the stay frequency, and the stay pattern. The stay time is measured taking into consideration the type of data, measurement method, evaluation criteria, etc. For example, the tracking unit can measure the user's stay time in each space and grasp the usage status. The tracking unit can suggest efficient space use based on the stay time data. For example, the tracking unit can analyze the user's stay time and provide the optimal stay time. In this way, by measuring the user's stay time in each space, efficient space use can be suggested. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or without AI. For example, the tracking unit can input the stay time data to a generation AI, which can analyze the data to grasp the stay time.

[0077] The tracking unit can estimate the user's emotions and adjust the display method of the tracked information based on the estimated user's emotions. Examples of user emotions include stress, relaxation, and hurry. The user's emotions are estimated using an emotion estimation algorithm. For example, the tracking unit can analyze the user's facial expression data and estimate whether the user is stressed or relaxed. The tracking unit adjusts the display method of the tracked information based on the estimated user's emotions. For example, if the user is stressed, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. This allows the display method of information to be adjusted based on the user's emotions, thereby enabling a display that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or without AI. For example, the tracking unit can input user emotional data into the generation AI, which can then analyze the data and adjust how the information is displayed.

[0078] The tracking unit can use device information of users of each space during tracking. The device information includes, for example, smartphone location information, device type, and device usage status. The device information is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the tracking unit can use the location information of the smartphones of users of each space to grasp usage status in real time. The tracking unit can suggest efficient space usage based on the device information. For example, the tracking unit can analyze the user's device information and provide an optimal flow line. As a result, by using the device information of users of each space, usage status can be grasped in real time. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input device information to a generation AI, which can analyze the data and grasp usage status.

[0079] During tracking, the tracking unit can grasp the usage status based on the schedule information of the user of each space. The schedule information includes, for example, calendar information, the type of schedule, and the time of the schedule. The schedule information is collected taking into consideration the type of data, the collection method, the evaluation criteria, and the like. For example, the tracking unit can refer to the schedule information of the user of each space to grasp the usage status. The tracking unit can suggest efficient space usage based on the schedule information. For example, the tracking unit can analyze the user's schedule information and provide the optimal usage time. As a result, efficient space usage can be suggested by referring to the schedule information of the user of each space. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the schedule information to a generation AI, which can analyze the data to grasp the usage status.

[0080] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. Examples of user emotions include stress, relaxation, and hurry. The user emotions are estimated using an emotion estimation algorithm. For example, the suggestion unit can analyze the user's facial expression data to estimate whether the user is stressed or relaxed. The suggestion unit prioritizes suggestions based on the estimated user emotions. For example, if the user is stressed, important suggestions can be prioritized and addressed quickly. If the user is relaxed, detailed suggestions can be provided for later reference. This enables suggestions tailored to the user's needs by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input the user's emotional data into the generation AI, which can then analyze the data and determine the priority of suggestions.

[0081] When making a proposal, the suggestion unit can collect user feedback for each space and optimize the proposal content. The feedback includes, for example, user opinions, evaluations, and areas for improvement. The feedback is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the suggestion unit can collect user feedback for each space and optimize the proposal content. The suggestion unit can propose efficient space use based on the feedback data. For example, the suggestion unit can analyze user feedback and provide optimal suggestions. As a result, by collecting user feedback for each space, the proposal content can be optimized and efficient space use can be achieved. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input feedback data to a generation AI, which can analyze the data and optimize the proposal content.

[0082] When making a proposal, the suggestion unit can customize the proposal content based on the work content of the user of each space. The work content includes, for example, the type of work, the importance of the work, and the frequency of the work. The work content is collected taking into consideration the type of data, the collection method, the evaluation criteria, and the like. For example, the suggestion unit can make optimal proposals based on the work content of the user of each space. The suggestion unit can propose efficient space utilization based on the work content data. For example, the suggestion unit can analyze the user's work content and propose an optimal layout. This enables optimal proposals to be made by customizing the proposal content based on the work content of the user of each space. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input work content data into a generation AI, which can analyze the data and customize the proposal content.

[0083] The suggestion unit can estimate the user's emotions and adjust the display method of suggestions based on the estimated user emotions. Examples of user emotions include stress, relaxation, and hurry. The user emotions are estimated using an emotion estimation algorithm. For example, the suggestion unit can analyze the user's facial expression data and estimate whether the user is stressed or relaxed. The suggestion unit adjusts the display method of suggestions based on the estimated user emotions. For example, if the user is stressed, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. This enables display according to the user's needs by adjusting the display method of suggestions based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotion data into a generation AI, which can analyze the data and adjust the display method of suggestions.

[0084] When making a proposal, the suggestion unit can optimize the proposal content based on the user's past usage history of each space. The usage history includes, for example, the number of times each space is used, the usage time, and user feedback. The usage history is collected taking into consideration the type of data, collection period, collection method, etc. For example, the suggestion unit can refer to the user's past usage history of each space and make optimal suggestions. The suggestion unit can propose efficient space use based on the usage history data. For example, the suggestion unit can analyze the user's past usage history and propose an optimal layout. As a result, by referring to the user's past usage history of each space, the proposal content can be optimized and efficient space use can be achieved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input usage history data to a generation AI, which can analyze the data and optimize the proposal content.

[0085] When making a proposal, the suggestion unit can customize the proposal content based on the geographical location information of the user of each space. The geographical location information includes, for example, the user's current location, travel route, and place of stay. The geographical location information is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the suggestion unit can make optimal proposals by taking into consideration the geographical location information of the user of each space. The suggestion unit can propose efficient space utilization based on the geographical location information. For example, the suggestion unit can analyze the user's geographical location information and propose an optimal layout. This enables optimal proposals by taking into consideration the geographical location information of the user of each space. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input geographical location information to a generation AI, which can analyze the data and customize the proposal content.

[0086] The simulation unit can estimate the user's emotions and prioritize simulations based on the estimated user emotions. Examples of user emotions include stress, relaxation, and hurry. The user's emotions are estimated using an emotion estimation algorithm. For example, the simulation unit can analyze the user's facial expression data and estimate whether the user is stressed or relaxed. The simulation unit prioritizes simulations based on the estimated user emotions. For example, if the user is stressed, important simulations can be prioritized and addressed quickly. If the user is relaxed, detailed simulations can be performed and made available for later reference. By prioritizing simulations based on the user's emotions, simulations tailored to the user's needs can be performed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the simulation unit can be performed using, for example, an AI or without an AI. For example, the simulation unit can input the user's emotional data into the generation AI, which can then analyze the data and determine the priority of the simulation.

[0087] During the simulation, the simulation unit analyzes user movement lines in each space and can confirm the effects of layout changes in detail. The movement lines include, for example, user movement routes, stay times, and congestion levels. The movement lines are analyzed taking into account the type of data, analysis method, evaluation criteria, and the like. For example, the simulation unit can analyze user movement lines in each space and confirm the effects of layout changes. The simulation unit can propose an efficient layout based on the movement line data. For example, the simulation unit can analyze user movement lines and provide optimal movement lines. As a result, an efficient layout can be proposed by analyzing user movement lines in each space. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input movement line data into a generation AI, which can analyze the data and understand the movement lines.

[0088] During the simulation, the simulation unit measures the user's stay time in each space and can confirm the effect of the layout change in detail. The stay time includes, for example, the time spent by the user in each space, the frequency of stay, and the stay pattern. The stay time is measured taking into account the type of data, measurement method, evaluation criteria, etc. For example, the simulation unit can measure the user's stay time in each space and confirm the effect of the layout change. The simulation unit can propose efficient space use based on the stay time data. For example, the simulation unit can analyze the user's stay time and provide an optimal stay time. As a result, by measuring the user's stay time in each space, efficient space use can be proposed. Some or all of the above-mentioned processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the stay time data into a generation AI, which can analyze the data and determine the stay time.

[0089] The simulation unit can estimate the user's emotions and adjust the display method of the simulation based on the estimated user's emotions. Examples of user emotions include stress, relaxation, and hurry. The user's emotions are estimated using an emotion estimation algorithm. For example, the simulation unit can analyze the user's facial expression data and estimate whether the user is stressed or relaxed. The simulation unit adjusts the display method of the simulation based on the estimated user's emotions. For example, if the user is stressed, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. This allows the display to be tailored to the user's needs by adjusting the display method of the simulation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the simulation unit can be performed using, for example, an AI. For example, the simulation unit can input the user's emotion data into a generation AI, which can analyze the data and adjust the display method of the simulation.

[0090] The simulation unit can use device information of users in each space during simulation. The device information includes, for example, smartphone location information, device type, and device usage status. The device information is collected taking into consideration the type of data, collection method, evaluation criteria, and the like. For example, the simulation unit can use smartphone location information of users in each space to perform a simulation in real time. The simulation unit can propose efficient space usage based on the device information. For example, the simulation unit can analyze user device information and provide optimal traffic lines. This allows for real-time simulations by using the device information of users in each space. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input device information into a generation AI, which then analyzes the data and performs a simulation.

[0091] During the simulation, the simulation unit can confirm the effect of the layout change by referring to the schedule information of the users of each space. The schedule information includes, for example, calendar information, the type of schedule, and the time of the schedule. The schedule information is collected taking into consideration the type of data, the collection method, the evaluation criteria, and the like. For example, the simulation unit can confirm the effect of the layout change by referring to the schedule information of the users of each space. The simulation unit can propose efficient space utilization based on the schedule information. For example, the simulation unit can analyze the user's schedule information and provide an optimal layout. As a result, efficient space utilization can be proposed by referring to the user's schedule information of each space. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the schedule information to a generation AI, which can analyze the data and confirm the effect of the layout change. === Hard Collateral 1-1 === Each of the multiple elements, including the database creation unit, tracking unit, suggestion unit, and simulation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the database creation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The tracking unit is implemented using the sensors and camera 42 of the smart device 14, and the specific processing unit 290 of the data processing device 12 tracks the usage status of each space in real time. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes efficient space utilization methods using machine learning. The simulation unit is implemented by the specific processing unit 290 of the data processing device 12 and simulates the effects of layout changes. Furthermore, the database creation unit using an emotion estimation algorithm estimates the user's emotion using the camera 42 and microphone 38B of the smart device 14, and the specific processing unit 290 of the data processing device 12 determines the priority of the information. === Hard Collateral 1-2 === Each of the multiple elements, including the database creation unit, tracking unit, suggestion unit, and simulation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the database creation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The tracking unit is realized using the sensors and camera 42 of the smart glasses 214, and the specific processing unit 290 of the data processing device 12 tracks the usage status of each space in real time. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an efficient space utilization method using machine learning. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates the effect of layout changes. Furthermore, the database creation unit using an emotion estimation algorithm estimates the user's emotion using the camera 42 and microphone 238 of the smart glasses 214, and the specific processing unit 290 of the data processing device 12 determines the priority of the information. === Hard Collateral 1-3 === Each of the multiple elements, including the database creation unit, tracking unit, suggestion unit, and simulation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the database creation unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. The tracking unit is realized using the sensors and camera 42 of the headset-type terminal 314, and the specific processing unit 290 of the data processing device 12 tracks the usage status of each space in real time. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes efficient space utilization methods using machine learning. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates the effects of layout changes. Furthermore, the database creation unit using an emotion estimation algorithm estimates the user's emotion using the camera 42 and microphone 238 of the headset-type terminal 314, and the specific processing unit 290 of the data processing device 12 determines the priority of the information. === Hard Collateral 1-4 === Each of the multiple elements, including the database creation unit, tracking unit, suggestion unit, and simulation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the database creation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The tracking unit is realized using the sensors and camera 42 of the robot 414, and the specific processing unit 290 of the data processing device 12 tracks the usage status of each space in real time. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests efficient space utilization methods using machine learning. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates the effects of layout changes. Furthermore, the database creation unit using an emotion estimation algorithm estimates the user's emotions using the camera 42 and microphone 238 of the robot 414, and the specific processing unit 290 of the data processing device 12 determines the priority of the information.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The suggestion unit can estimate the user's emotions and customize the content of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize suggestions for spaces where the user can relax. If the user wants to concentrate, it can suggest spaces that provide a quiet environment. Furthermore, if the user is in a hurry, it can suggest a space that allows the user to reach the desired space via the shortest route. This makes it possible to make optimal suggestions according to the user's emotions.

[0094] The tracking unit collects health data from users of each space and can grasp usage status in detail. For example, it can collect data such as a user's heart rate, number of steps, and calorie consumption to monitor their health status. This makes it possible to suggest space usage based on their health status. For example, if the heart rate is high, it can suggest a space where you can relax, and if the number of steps is low, it can suggest a space where you can exercise.

[0095] The database creation unit can collect user preference data for each space and create a database. For example, it can collect preference data such as the user's favorite music, drinks, and comfortable chairs, and suggest the optimal space for the user. This makes it possible to suggest spaces customized according to the user's preferences. For example, it can suggest spaces that play the user's favorite music or that serve the user's favorite drinks.

[0096] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can immediately suggest a space where the user can relax. If the user is concentrating, the suggestion unit can refrain from making a suggestion and notify the user later. Furthermore, if the user is in a hurry, the suggestion unit can quickly make a suggestion and provide a space that can be used immediately. This makes it possible to make suggestions at the optimal timing according to the user's emotions.

[0097] The simulation unit collects energy consumption data from users in each space and can confirm the effects of layout changes in detail. For example, it can collect energy consumption data for lighting and air conditioning in each space and propose energy-efficient layouts. This enables efficient space use with minimal energy consumption. For example, it can propose lighting layouts that make use of natural light and efficient air conditioning layouts.

[0098] The tracking unit can estimate the user's emotions and adjust the level of detail of the information to be tracked based on the estimated user's emotions. For example, if the user is feeling stressed, important information can be tracked preferentially and detailed information can be provided. If the user is relaxed, only basic information can be tracked and detailed information can be provided later. This makes it possible to provide optimal information according to the user's emotions.

[0099] The database unit can collect communication data of users in each space and store it in a database. For example, it can collect communication data such as the content of conversations between users and email exchanges, and suggest spaces where efficient communication can take place. This facilitates communication and improves work efficiency. For example, it can suggest layouts that make conversations easy and spaces that maintain privacy.

[0100] The suggestion unit can estimate the user's emotions and personalize the content of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, a space where the user can relax can be suggested. If the user wants to concentrate, a space that provides a quiet environment can be suggested. Furthermore, if the user is in a hurry, a suggestion can be made that allows the user to reach the desired space via the shortest route. This makes it possible to make optimal suggestions according to the user's emotions.

[0101] The simulation unit collects productivity data for users in each space and can confirm the effects of layout changes in detail. For example, it can measure the work efficiency and concentration levels of users in each space and propose layouts that are expected to improve productivity. This improves work efficiency and enables more productive space usage. For example, it can propose desk arrangements that encourage concentration and meeting room layouts that facilitate smooth communication.

[0102] The suggestion unit can estimate the user's emotions and customize the display method of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that quickly provides information can be adopted. This enables the optimal display method to be used according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The database creation unit creates a database of information about each space in the office. Information about each space in the office includes, but is not limited to, information about the area, facilities, and usage status. The database creation unit creates a database of information about each space using, for example, cloud storage. Cloud storage is used to streamline data storage and access methods. Step 2: The tracking unit tracks the usage status of each space in real time based on the information compiled into a database by the database compilation unit. The tracking unit tracks the usage status of each space in real time using, for example, a temperature sensor, a motion sensor, and a pressure sensor for grasping the occupancy status of a desk. The temperature sensor measures the temperature of each space, and the motion sensor detects the presence of a person in the space. The pressure sensor is used to grasp the occupancy status of a desk. Step 3: The suggestion unit proposes partitions based on the organization's needs based on the data collected by the tracking unit. For example, the suggestion unit predicts future space demand based on past usage data and allocates resources appropriately. The suggestion unit can also use machine learning to analyze usage patterns and propose efficient space utilization methods. Step 4: The simulation unit simulates the layout proposed by the proposal unit. The simulation unit performs a simulation to confirm the effect of a layout change in advance, for example. The simulation unit is used to evaluate the effect of the layout change and propose an optimal layout.

[0105] 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.

[0106] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 department that creates a database of information for each space in the office, a tracking unit that tracks the usage status of each space in real time based on the information databased by the database creation unit; a suggestion unit that makes partition suggestions according to the needs of the organization based on the data collected by the tracking unit; The system includes a simulation unit that simulates the layout proposed by the proposal unit. A system characterized by:

2. The aforementioned tracking unit is Using temperature sensors or motion sensors, and pressure sensors to monitor desk occupancy, the system tracks the usage of each space in real time. The system of claim 1 .

3. The aforementioned database unit, Use cloud storage to create a database of information for each space The system of claim 1 .

4. The proposal unit Predict future space demand and allocate resources based on past usage data The system of claim 1 .

5. The aforementioned simulation unit, Conduct simulations to confirm the effects of layout changes in advance The system of claim 1 .

6. The proposal unit Using machine learning to analyze usage patterns and suggest efficient space utilization methods The system of claim 1 .

7. The proposal unit We propose parcels using algorithms such as random forests or support vector machines. The system of claim 1 .

8. The aforementioned database unit, Estimate the user's emotions and determine the priority of information to be stored in the database based on the estimated user emotions. The system of claim 1 .

Images