system

Abstract

We provide the system. [Solution] A sensor device for acquiring usage data, An information processing device for storing data received from the aforementioned sensor device in a database, An artificial intelligence model for analyzing data stored in the aforementioned information processing device and generating optimal user guidance based on the analysis results, A communication device for notifying the user of the generated user guide, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern office environment, it is required to appropriately manage the reservation status and attendance information of conference rooms, and to achieve efficient utilization of resources and smooth communication. However, in conventional systems, real-time information sharing and prediction are not sufficiently performed, resulting in overlapping conference room reservations and confusion among users. It is necessary to solve such problems and optimize the efficient utilization of resources and communication in the office.

Means for Solving the Problems

[0005] This invention collects real-time information on meeting rooms and occupancy by using a sensor device to acquire usage data. Furthermore, the data obtained from the sensor device is managed by an information processing device and stored in a database. This makes it possible to centrally understand past usage status. In addition, the information processing device uses an artificial intelligence model to analyze the collected data and generates optimal meeting room usage guidance based on the analysis results. The generated guidance is notified via a communication device and provided to the user in real time. As a result, users can efficiently utilize the optimal resources and prevent overlapping and confusion in meeting reservations.

[0006] A "sensor device" is a device that detects physical information about the environment and acquires it as digital data.

[0007] An "information processing device" is a device that stores acquired data in an analyzable format and performs database management and analysis.

[0008] An "artificial intelligence model" is a program structure that uses machine learning algorithms to analyze data and perform predictions and optimizations.

[0009] A "communication device" is a device that includes a network interface for transmitting generated information and notifications to the user.

[0010] A "database" is a system that systematically organizes acquired information and stores it in a way that allows for later access. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units 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), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention provides a system to support efficient communication and resource management in an office. This system uses the following devices and methods to collect meeting room and occupancy information in real time and manage usage status.

[0033] Device configuration

[0034] The system consists of multiple sensor devices installed in the conference rooms, an information processing device for managing the data, an artificial intelligence model for analyzing the data, and a communication device for sending notifications. Each conference room is equipped with sensor devices such as temperature sensors and motion sensors to continuously acquire data on the room's usage.

[0035] Operating principle

[0036] Each conference room is equipped with a sensor device, which periodically collects information such as the number of people in the room, temperature, and humidity. This data is transmitted to an information processing unit via a communication protocol.

[0037] The server stores data received from sensors in a database and analyzes it using an artificial intelligence model. The AI ​​model learns from past data and patterns to predict future meeting room usage trends. Based on these predictions, it generates optimal meeting room reservations and alternative options for the user.

[0038] Users can view real-time information and book meeting rooms through interfaces provided via mobile applications or PCs. Furthermore, they can adjust their schedules based on suggestions from the system.

[0039] Specific example

[0040] For example, if a user tries to book a meeting room at 10 AM, the server checks existing booking information and detects that the time slot is already fully booked. In this case, the server uses an artificial intelligence model to suggest other available meeting rooms or nearby time slots to help the user make the best choice. Furthermore, if sensors detect overcrowding in a meeting room, the server notifies the user of a suggestion to reschedule the meeting, ensuring a more comfortable environment by avoiding overcrowding.

[0041] Thus, the present invention aims to achieve efficient management of meeting rooms and resources within an office, as well as smooth communication with users. This system contributes to improving office productivity by optimizing resources.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The terminal uses sensors installed in the conference room to acquire real-time data such as room temperature, people's movements, and usage status. The sensors collect data every 10 seconds and transmit it to the server via wireless communication.

[0045] Step 2:

[0046] The server receives the transmitted data and stores it in the database. During this process, the accuracy of the data is verified, and the data format is standardized as needed.

[0047] Step 3:

[0048] The server uses accumulated data to perform analysis using an artificial intelligence model. This analysis predicts booking trends for the following week and month based on past usage patterns of meeting rooms.

[0049] Step 4:

[0050] The server generates optimal meeting room usage recommendations based on the prediction results. For example, if congestion is predicted during a specific time slot, it will automatically suggest other available time slots or meeting rooms.

[0051] Step 5:

[0052] The server generates a notification and sends it to each user. Users receive this information via a mobile app or PC and adjust their schedules accordingly.

[0053] Step 6:

[0054] The system reserves meeting rooms based on the information provided by the user. Once the user confirms the reservation, the server updates the reservation status and manages the usage schedule in conjunction with the sensor system.

[0055] Step 7:

[0056] As the meeting start time approaches, the device re-checks the situation using sensors to detect whether the number of participants and room temperature are appropriate. If necessary, it automatically adjusts the environmental settings.

[0057] Step 8:

[0058] Users submit feedback via a mobile app after the meeting. The server receives this feedback and uses it to improve analysis results and predictive models.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In office and other business spaces, there is a need to efficiently manage the usage of meeting rooms and equipment, optimizing utilization while avoiding waste and congestion. Traditional systems suffer from insufficient real-time capabilities and future usage forecasting, leading to resource overuse and inappropriate bookings. Furthermore, a lack of appropriate and prompt notifications to users makes efficient time management difficult.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes a sensor means for acquiring usage status information, a processing means for storing the information received from the sensor means in a storage device, and an artificial intelligence means for analyzing the information stored in the processing means and generating optimal usage guidance based on the analysis results. This enables real-time monitoring of the usage status of meeting rooms and equipment, efficient reservation management, and prediction of future usage.

[0064] "Usage status information" refers to data regarding the current usage status and environmental conditions of meeting rooms and equipment.

[0065] "Sensor means" refers to devices or equipment installed to acquire usage information from the physical environment.

[0066] A "memory device" refers to a device or system that has the function of structuring and storing acquired usage information.

[0067] "Processing means" refers to a device or software that has the function of analyzing information stored in a memory device or communicating with other devices or systems.

[0068] "Artificial intelligence tools" are those that have the function of learning past usage patterns and executing algorithms and models to predict future usage.

[0069] "Communication means" refers to the network or devices used to notify users of the generated usage instructions.

[0070] "User guide" refers to information generated based on analysis and predictions, intended to present users with the optimal usage methods and alternative options.

[0071] This invention aims to efficiently manage meeting rooms and equipment in an office environment and to provide users with timely information. It mainly consists of sensor means, storage devices, processing means, artificial intelligence means, and communication means.

[0072] Each meeting room is equipped with motion sensors and temperature sensors, which act as terminals to periodically acquire information about the room's usage. This information is stored digitally in a storage device.

[0073] The server analyzes the data collected in the storage device. As a preprocessing step, it cleanses and normalizes the data, and uses a generative AI model to predict future usage trends from past data. For example, if usage is high on a particular day of the week or time slot, it has a function to warn users in advance that they should be careful when making reservations for the same time slot the following week.

[0074] Users receive this information via PCs or mobile devices. They can obtain usage instructions generated via communication in real time and make reservations based on the optimal usage instructions and alternatives provided by the system. This allows for efficient resource utilization while avoiding congestion.

[0075] For example, if a user enters the prompt "I would like to reserve a meeting room for 10:00 AM next Wednesday," the server will use an AI model to suggest alternative times and meeting rooms, as that time slot may be busy. In this way, the system achieves effective resource management and a smooth user experience.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The terminal uses motion sensors and temperature sensors installed in the conference room to acquire information about the number of people in the room and the environment. It receives real-time physical data as input and converts it into a digital format. As output, it generates converted usage information and transmits it to the server via a communication protocol.

[0079] Step 2:

[0080] The server receives usage information sent from terminals. It takes data packets from each meeting room as input and saves that data to a database in a structured format as output. Specifically, it performs data validation and cleansing (removing unnecessary data and errors) to conform to the storage format.

[0081] Step 3:

[0082] The server begins data analysis based on the stored data. It uses cleansed historical usage information as input and employs a generative AI model for analysis. The output generates meeting room usage trends and future usage predictions. Specifically, it performs time-series analysis and pattern recognition to extract usage trends for specific days of the week and time slots.

[0083] Step 4:

[0084] The server generates user guidance based on the analysis results. It uses predictive data generated by an AI model as input and outputs optimal meeting room reservation guidance and alternative options. Specifically, it generates prompt messages to avoid time slots that are already booked and provides these to the user along with next-best options.

[0085] Step 5:

[0086] Users receive real-time usage information from the server via their PC or mobile device. Their input is receiving notifications from the server, and their output is confirming their meeting room reservation based on that information. Specifically, they complete the reservation by reviewing and selecting the options suggested on the user interface.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] The challenge lies in efficiently managing the usage of meeting rooms and break spaces within the factory and providing users with appropriate usage guidance to optimize resources and improve employee convenience. In particular, real-time information provision and the presentation of alternative solutions are required to respond to sudden changes and requests.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes a detection device means for acquiring usage status data, an information processing means for storing the data received from the detection device in a recording device, and a machine learning model means for analyzing the data stored in the information processing means and generating optimal usage guidance based on the analysis results. This makes it possible to optimize the usage status of meeting rooms and break spaces within a factory and provide users with quick and appropriate guidance.

[0092] "Usage data" refers to information about the use of meeting rooms and break spaces acquired by detection devices, and includes data such as the number of people, duration, temperature, and frequency of use.

[0093] A "detection device" refers to a sensor or device installed in meeting rooms or break areas to collect usage data.

[0094] "Information processing means" refers to a system or computer that has the function of storing data received from a detection device in a recording device and processing or calculating the data as needed.

[0095] A "recording device" is a storage medium or system that stores data acquired by information processing means and allows it to be retrieved as needed.

[0096] A "machine learning model" is an algorithm or program that utilizes artificial intelligence technology to learn from past data and predict future usage based on new data.

[0097] "Usage Guide" refers to information generated based on analysis results, which suggests the optimal way for users to utilize meeting rooms and break spaces.

[0098] A "transmitting device" is a device or system that has a communication function to notify users of the generated usage instructions.

[0099] A "predictive model" is a model that uses statistical or machine learning methods to predict future usage of meeting rooms and break spaces based on past usage patterns.

[0100] To implement this invention, it is necessary to build a system that collects usage data by installing multiple detection devices in meeting rooms and break areas within a factory. Suitable detection devices include temperature sensors and motion sensors. This data is transmitted to and managed by an information processing system. The information processing system uses edge computing devices such as Raspberry Pi and Intel NUC to save the data to a recording device in real time. The saved data is analyzed by machine learning models using machine learning frameworks such as TENSORFLOW® and PyTorch to predict usage trends.

[0101] The server uses a machine learning model that has learned past usage patterns to predict future usage and generate optimal usage guidance. This guidance is sent to the user's smartphone or tablet via a transmission device. The transmission device uses a device equipped with communication capabilities such as Wi-Fi or Bluetooth.

[0102] For example, if a meeting room is urgently needed at a certain time, the system quickly detects available rooms and presents the user with the best option. The user receives this notification via a smartphone app and can immediately book a room. It can also predict and notify the user of the next available room and time.

[0103] Examples of prompts include, "Please tell me the current reservation status and availability of each meeting room," or "Which meeting room is available next?" This allows the generative AI model to analyze the usage situation and provide the most appropriate answer.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The terminal collects usage data from detection devices (temperature sensors and motion sensors) installed within the factory. This data includes the number of people in the room, the temperature, and the frequency of use. The terminal periodically measures this data and converts it into a packet format for storage. This prepares the collected data for transmission to the information processing system.

[0107] Step 2:

[0108] The server receives usage data sent from the terminal. The received data is stored in a database using information processing tools. SQL databases or NoSQL databases are used for data storage. This ensures that the usage data is recorded for subsequent analysis.

[0109] Step 3:

[0110] The server inputs data stored in the database into a machine learning model. Using the machine learning model (which uses TensorFlow or PyTorch), it learns past usage patterns and predicts future usage. This outputs predictive data to generate optimal usage guidance.

[0111] Step 4:

[0112] The server analyzes the generated usage instructions and creates an appropriate notification for the user. This notification includes the next available meeting room and the optimal usage time. The generated notification is converted to text format and passed to the sending device. This completes the preparation for the notification to the user.

[0113] Step 5:

[0114] The transmitting device sends notification information received from the server to the user's smartphone or tablet. Notifications are delivered in real time via Bluetooth or Wi-Fi. Users receive these notifications and use them to book meeting rooms or adjust their schedules. As a result, users can make rational decisions based on the guidance from the system.

[0115] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0116] This invention incorporates a function to recognize user emotions into a system that supports efficient resource management and smooth communication in an office environment. This system accurately grasps the usage status of meeting rooms and the emotional state of users, and makes optimal suggestions based on that information.

[0117] Device configuration

[0118] This system consists of a sensor device, an information processing device, an artificial intelligence model, a communication device, and an emotion engine. The sensor device collects physical data such as the temperature and number of people in the meeting room. The emotion engine is used to analyze the user's voice data and recognize the user's emotional state.

[0119] Operating principle

[0120] The terminal uses multiple sensors placed in the conference room to grasp the physical state of the environment, while simultaneously inputting the user's voice into an emotion engine. This emotion engine analyzes the voice data in real time to determine the user's emotional state.

[0121] The server receives data from sensor devices and an emotion engine and stores it in a database using an information processing device. Based on this data, an artificial intelligence model makes predictions about the usage status of meeting rooms and the emotional state of users.

[0122] Based on the prediction results, the server generates meeting room reservation information and notifications tailored to the user's emotional state. For example, if the server determines that the user is stressed, it will use a warm and friendly notification message and suggest a schedule with fewer meetings.

[0123] Users can receive suggested meeting room reservation information and recommendations via mobile devices or computers. They can also accept suggestions for communication methods that cater to their emotional state, enabling the creation of a more comfortable meeting environment.

[0124] Specific example

[0125] For example, if a user asks a question using voice input during a meeting, and the emotion engine detects the user's stress from the audio, the server will check the user's current meeting schedule and suggest adjusting the next meeting to a more relaxed one. It is also possible to reduce the user's psychological burden by sending relaxation reminders.

[0126] In this way, the present invention, by incorporating emotion recognition technology, provides a new form of office support system that more subtly and comprehensively improves communication and resource management in the office environment and takes into account the psychological well-being of users.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The terminal uses sensors installed in the conference room to acquire real-time data such as room temperature, humidity, and the number of people. This data is collected at regular intervals and sent to a server.

[0130] Step 2:

[0131] The device inputs the user's voice data into the emotion engine and performs voice analysis. The emotion engine generates analysis results and identifies the user's emotional state.

[0132] Step 3:

[0133] The server collects all data received from sensors and the emotion engine and stores it in a database. During this process, the data integrity is verified and converted into the format necessary for analysis.

[0134] Step 4:

[0135] The server analyzes the collected data using an artificial intelligence model. This analysis predicts the usage of meeting rooms and determines appropriate responses based on the users' emotional states.

[0136] Step 5:

[0137] Based on the analysis results, the server generates optimal meeting room schedules and suggestions that take into account the user's emotional state. For example, it can create schedule adjustments to reduce the user's burden and notifications that include positive messages.

[0138] Step 6:

[0139] Users receive suggested information via mobile apps or PCs. Based on this, users can adjust meeting room reservations and consider other suggestions for emotional support.

[0140] Step 7:

[0141] The terminal continuously supplies audio data to the emotion engine even while the meeting is in progress. If the user's emotions change, the system can make dynamic adjustments accordingly.

[0142] Thus, this system can grasp the user's emotional state in real time and provide optimal meeting room usage guidance and flexible scheduling suggestions accordingly.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] In modern office environments, insufficient utilization of meeting rooms and adequate consideration of users' emotional well-being are leading to problems such as stress and decreased productivity. To address this issue, a system is needed that integrates and analyzes environmental information and emotional states to provide appropriate recommendations.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes a detection device for measuring environmental state data, an emotion analysis device for analyzing voice data and determining the user's emotional state, and an information processing device for integrating the data and storing it in a memory area. This enables the automation of optimal meeting room usage suggestions and emotional care based on environmental and emotional information.

[0148] "Environmental condition data" refers to information that indicates physical conditions such as temperature, humidity, and the number of people in a workspace, such as an office.

[0149] A "detection device" is a device that includes sensors used to acquire environmental condition data.

[0150] "Voice data" refers to the waveform information of the sounds spoken by the user, and is data that is subject to emotion analysis in real time.

[0151] An "emotion analysis device" is a combination of hardware and software that analyzes voice data to determine the user's emotional state.

[0152] "Memory area" refers to a database or storage area where acquired data is stored and managed for later analysis and use.

[0153] An "information processing device" is a computer system that integrates and stores data collected from detection devices and emotion analysis devices.

[0154] A "generative AI model" is an artificial intelligence algorithm that uses stored data for analysis and automatically generates optimal suggestions.

[0155] "Communication methods" refer to technologies such as the internet and wireless communication used to deliver suggestions and notifications generated by a server to users.

[0156] "Suggestions" refer to information that includes improvement measures and recommended actions provided to users based on the analysis results.

[0157] This invention is a system aimed at efficient resource management and improved communication in an office environment, accurately understanding user emotions and making optimal suggestions based on that information. The main components of the system include a detection device that measures the physical state of the environment, an emotion analysis device that analyzes voice data, and an information processing device that integrates and manages the data.

[0158] The server uses sensing devices to measure environmental conditions. This allows for the real-time collection of data such as temperature, humidity, and the number of people in the room. Simultaneously, it uses an emotion analysis device to analyze the user's voice. The emotion analysis device processes the voice data and recognizes the user's emotional state in real time. This process utilizes speech recognition technology and natural language processing technology.

[0159] The collected data is integrated into an information processing unit on the server and stored in its memory. The information processing unit utilizes a generative AI model to analyze this data and generate optimal suggestions. This model has the ability to predict future usage patterns and emotional states by learning from past data.

[0160] The suggested content will be notified to the user via communication means. Users can receive these suggestions via mobile devices or computers, and use them to help adjust meetings and manage their emotional state. For example, if emotion analysis determines that the user is experiencing stress, it will be recommended to suggest a more relaxing schedule for the next meeting or to send a warm message.

[0161] As a concrete example, one could input a prompt message such as, "When a user is feeling stressed during a meeting, how should we suggest ways to reduce their psychological burden?" This would allow the system to provide appropriate feedback and improve the quality of communication in the office environment.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The terminal receives environmental data from sensors in the conference room. This data includes temperature, humidity, and the number of people in the room. The terminal converts this raw data into digital data and prepares it for transmission to the server. At this point, the input is analog data from the sensors, and the output is digital data that can be sent to the server.

[0165] Step 2:

[0166] The terminal inputs the user's voice data into an emotion analysis device. This input is received as an audio signal and processed by the emotion analysis device. This device uses speech recognition technology to measure the characteristics of the voice and extract features to identify the user's emotions. The output here is attribute data indicating the user's emotional state.

[0167] Step 3:

[0168] The server receives environmental state data and sentiment analysis data transmitted from the terminal. The received data is integrated by an information processing device, organized, and stored in memory. This operation involves integrating data in different formats and assigning timestamps. The integrated data is then used for subsequent analysis.

[0169] Step 4:

[0170] The server performs analysis based on a generative AI model using integrated data. This model learns from past usage patterns and sentiment data, and has the ability to predict future usage and sentiment. Based on the input data, the model generates optimal suggestions and prepares them for use in the next step. The output is the suggestions presented to the user.

[0171] Step 5:

[0172] The server notifies the user of the generated suggestions via communication channels. Suggestions, including scheduling adjustments and consideration of emotional state, are created and sent via mobile devices or computers. The user receives specific action instructions and warm, supportive response messages from the system.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0175] In traditional brick-and-mortar stores, providing services that take into account customers' emotional states was difficult, and there was a lack of concrete measures to improve customer satisfaction. Therefore, there was a need for a system that could analyze customers' emotional states in real time and provide appropriate services and suggestions based on that analysis.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes a detection device means for acquiring usage data and voice data, an information processing device means for storing the data received from the detection device means, and an artificial intelligence model means for analyzing the data stored in the information processing device means and generating appropriate suggestions according to the emotional state based on the analysis results. This makes it possible to provide services in a physical store that are tailored to the emotional state of the customer.

[0178] "Usage data" refers to information about customer behavior and activities within physical stores, including customer visit frequency and length of stay.

[0179] "Voice data" refers to digital information recorded from customer conversations and statements, and is used for analyzing emotional states.

[0180] "Detection device means" refers to equipment or sensors placed within the store to acquire usage data and audio data.

[0181] An "information processing device" is a computer system that stores data received from a detection device and facilitates its analysis.

[0182] "Artificial intelligence model means" refers to a function that includes an algorithm that analyzes accumulated data and determines the emotional state of customers.

[0183] "Communication device means" refers to a communication device used to notify store staff or systems of generated proposals and service details.

[0184] The system implementing this invention evaluates customer behavior in physical stores and provides services based on customer emotions. The server collects usage data and voice data using detection devices installed in the store. The detection devices used include microphones for voice capture and sensors that track customer movements. This data is stored by an information processing device.

[0185] The information processing device has a program installed that processes the received data and performs data analysis using an artificial intelligence model. The artificial intelligence model analyzes the customer's emotions from the voice data and estimates their emotional state based on that analysis. The technologies used in this process include natural language processing and machine learning algorithms, which improve the accuracy of the analysis.

[0186] The analyzed data is transmitted by the server to a communication device, which notifies store staff in real time of service suggestions based on the customer's emotional state. The communication device functions as a display or mobile device for staff, showing them what kind of service is appropriate for the customer.

[0187] For example, if a customer's stress level is detected through their voice during a store visit, the server sends a notification to the staff saying, "Please suggest products that will help the customer relax." This allows the staff to provide customer-tailored service. An example of a prompt message to input to the AI ​​model would be, "Recognize the customer's emotions and, if they are feeling stressed, suggest what service prompts would be effective."

[0188] This will enable appropriate responses tailored to each customer's situation, significantly improving the quality of service at physical stores.

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The terminal acquires customer voice data through microphones and sensors installed in the store. The input is customer conversation and behavior, and the output is real-time digital voice data. This data undergoes initial processing such as noise reduction and time axis adjustment of the audio.

[0192] Step 2:

[0193] The server receives audio data transmitted from the terminal through an information processing device. The input is digital audio data from the terminal, and the output is stored in an audio database. During this process, the data is organized and associated metadata (date, time, location, etc.) is added.

[0194] Step 3:

[0195] The server analyzes the accumulated voice data using an artificial intelligence model. This model uses natural language processing and sentiment analysis algorithms to estimate the customer's emotional state from the input voice data. The output is the estimated emotion label (e.g., relaxed, stressed).

[0196] Step 4:

[0197] The server generates service suggestions corresponding to the emotional state based on the analysis results. The input is the emotional label obtained in step 3, and the output is service suggestions and prompt statements. This generation process includes searching for candidate suggestions from the database and optimizing them according to the individual situation.

[0198] Step 5:

[0199] The server transmits the service proposal generated through the transmission device to the store staff. The input is the service proposal generated in step 4, and the output is a notification displayed on the staff's display or mobile device. This notification includes specific customer service guidelines and product suggestions.

[0200] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0201] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0202] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0206] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0207] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0208] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0209] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0210] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0211] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0212] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0213] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0214] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0215] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0216] This invention provides a system to support efficient communication and resource management in an office. This system uses the following devices and methods to collect meeting room and occupancy information in real time and manage usage status.

[0217] Device configuration

[0218] The system consists of multiple sensor devices installed in the conference rooms, an information processing device for managing the data, an artificial intelligence model for analyzing the data, and a communication device for sending notifications. Each conference room is equipped with sensor devices such as temperature sensors and motion sensors to continuously acquire data on the room's usage.

[0219] Operating principle

[0220] Each conference room is equipped with a sensor device, which periodically collects information such as the number of people in the room, temperature, and humidity. This data is transmitted to an information processing unit via a communication protocol.

[0221] The server stores data received from sensors in a database and analyzes it using an artificial intelligence model. The AI ​​model learns from past data and patterns to predict future meeting room usage trends. Based on these predictions, it generates optimal meeting room reservations and alternative options for the user.

[0222] Users can view real-time information and book meeting rooms through interfaces provided via mobile applications or PCs. Furthermore, they can adjust their schedules based on suggestions from the system.

[0223] Specific example

[0224] For example, if a user tries to book a meeting room at 10 AM, the server checks existing booking information and detects that the time slot is already fully booked. In this case, the server uses an artificial intelligence model to suggest other available meeting rooms or nearby time slots to help the user make the best choice. Furthermore, if sensors detect overcrowding in a meeting room, the server notifies the user of a suggestion to reschedule the meeting, ensuring a more comfortable environment by avoiding overcrowding.

[0225] Thus, the present invention aims to achieve efficient management of meeting rooms and resources within an office, as well as smooth communication with users. This system contributes to improving office productivity by optimizing resources.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The terminal uses sensors installed in the conference room to acquire real-time data such as room temperature, people's movements, and usage status. The sensors collect data every 10 seconds and transmit it to the server via wireless communication.

[0229] Step 2:

[0230] The server receives the transmitted data and stores it in the database. During this process, the accuracy of the data is verified, and the data format is standardized as needed.

[0231] Step 3:

[0232] The server uses accumulated data to perform analysis using an artificial intelligence model. This analysis predicts booking trends for the following week and month based on past usage patterns of meeting rooms.

[0233] Step 4:

[0234] The server generates optimal meeting room usage recommendations based on the prediction results. For example, if congestion is predicted during a specific time slot, it will automatically suggest other available time slots or meeting rooms.

[0235] Step 5:

[0236] The server generates a notification and sends it to each user. Users receive this information via a mobile app or PC and adjust their schedules accordingly.

[0237] Step 6:

[0238] The system reserves meeting rooms based on the information provided by the user. Once the user confirms the reservation, the server updates the reservation status and manages the usage schedule in conjunction with the sensor system.

[0239] Step 7:

[0240] As the meeting start time approaches, the device re-checks the situation using sensors to detect whether the number of participants and room temperature are appropriate. If necessary, it automatically adjusts the environmental settings.

[0241] Step 8:

[0242] Users submit feedback via a mobile app after the meeting. The server receives this feedback and uses it to improve analysis results and predictive models.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0245] In office and other business spaces, there is a need to efficiently manage the usage of meeting rooms and equipment, optimizing utilization while avoiding waste and congestion. Traditional systems suffer from insufficient real-time capabilities and future usage forecasting, leading to resource overuse and inappropriate bookings. Furthermore, a lack of appropriate and prompt notifications to users makes efficient time management difficult.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes a sensor means for acquiring usage status information, a processing means for storing the information received from the sensor means in a storage device, and an artificial intelligence means for analyzing the information stored in the processing means and generating optimal usage guidance based on the analysis results. This enables real-time monitoring of the usage status of meeting rooms and equipment, efficient reservation management, and prediction of future usage.

[0248] "Usage status information" refers to data regarding the current usage status and environmental conditions of meeting rooms and equipment.

[0249] "Sensor means" refers to devices or equipment installed to acquire usage information from the physical environment.

[0250] A "memory device" refers to a device or system that has the function of structuring and storing acquired usage information.

[0251] "Processing means" refers to a device or software that has the function of analyzing information stored in a memory device or communicating with other devices or systems.

[0252] "Artificial intelligence tools" are those that have the function of learning past usage patterns and executing algorithms and models to predict future usage.

[0253] "Communication means" refers to the network or devices used to notify users of the generated usage instructions.

[0254] "User guide" refers to information generated based on analysis and predictions, intended to present users with the optimal usage methods and alternative options.

[0255] This invention aims to efficiently manage meeting rooms and equipment in an office environment and to provide users with timely information. It mainly consists of sensor means, storage devices, processing means, artificial intelligence means, and communication means.

[0256] Each meeting room is equipped with motion sensors and temperature sensors, which act as terminals to periodically acquire information about the room's usage. This information is stored digitally in a storage device.

[0257] The server analyzes the data collected in the storage device. As a preprocessing step, it cleanses and normalizes the data, and uses a generative AI model to predict future usage trends from past data. For example, if usage is high on a particular day of the week or time slot, it has a function to warn users in advance that they should be careful when making reservations for the same time slot the following week.

[0258] Users receive this information via PCs or mobile devices. They can obtain usage instructions generated via communication in real time and make reservations based on the optimal usage instructions and alternatives provided by the system. This allows for efficient resource utilization while avoiding congestion.

[0259] For example, if a user enters the prompt "I would like to reserve a meeting room for 10:00 AM next Wednesday," the server will use an AI model to suggest alternative times and meeting rooms, as that time slot may be busy. In this way, the system achieves effective resource management and a smooth user experience.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] The terminal uses motion sensors and temperature sensors installed in the conference room to acquire information about the number of people in the room and the environment. It receives real-time physical data as input and converts it into a digital format. As output, it generates converted usage information and transmits it to the server via a communication protocol.

[0263] Step 2:

[0264] The server receives usage information sent from terminals. It takes data packets from each meeting room as input and saves that data to a database in a structured format as output. Specifically, it performs data validation and cleansing (removing unnecessary data and errors) to conform to the storage format.

[0265] Step 3:

[0266] The server begins data analysis based on the stored data. It uses cleansed historical usage information as input and employs a generative AI model for analysis. The output generates meeting room usage trends and future usage predictions. Specifically, it performs time-series analysis and pattern recognition to extract usage trends for specific days of the week and time slots.

[0267] Step 4:

[0268] The server generates user guidance based on the analysis results. It uses predictive data generated by an AI model as input and outputs optimal meeting room reservation guidance and alternative options. Specifically, it generates prompt messages to avoid time slots that are already booked and provides these to the user along with next-best options.

[0269] Step 5:

[0270] Users receive real-time usage information from the server via their PC or mobile device. Their input is receiving notifications from the server, and their output is confirming their meeting room reservation based on that information. Specifically, they complete the reservation by reviewing and selecting the options suggested on the user interface.

[0271] (Application Example 1)

[0272] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] The challenge lies in efficiently managing the usage of meeting rooms and break spaces within the factory and providing users with appropriate usage guidance to optimize resources and improve employee convenience. In particular, real-time information provision and the presentation of alternative solutions are required to respond to sudden changes and requests.

[0274] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0275] In this invention, the server includes a detection device means for acquiring usage status data, an information processing means for storing the data received from the detection device in a recording device, and a machine learning model means for analyzing the data stored in the information processing means and generating optimal usage guidance based on the analysis results. This makes it possible to optimize the usage status of meeting rooms and break spaces within a factory and provide users with quick and appropriate guidance.

[0276] "Usage data" refers to information about the use of meeting rooms and break spaces acquired by detection devices, and includes data such as the number of people, duration, temperature, and frequency of use.

[0277] A "detection device" refers to a sensor or device installed in meeting rooms or break areas to collect usage data.

[0278] "Information processing means" refers to a system or computer that has the function of storing data received from a detection device in a recording device and processing or calculating the data as needed.

[0279] A "recording device" is a storage medium or system that stores data acquired by information processing means and allows it to be retrieved as needed.

[0280] A "machine learning model" is an algorithm or program that utilizes artificial intelligence technology to learn past data and predict future usage scenarios based on new data.

[0281] "Usage guidance" is information generated based on analysis results and proposes the optimal usage methods of meeting rooms and rest spaces to users.

[0282] A "transmission device" is a device or system with a communication function for notifying users of the generated usage guidance.

[0283] A "prediction model" is a model that uses statistical or machine learning methods to predict the future usage status of meeting rooms and rest spaces based on past usage patterns.

[0284] To implement this invention, it is necessary to build a system for collecting usage status data by installing multiple detection devices in meeting rooms and rest spaces within the factory. Suitable detection devices include temperature sensors and human presence sensors. This data is transmitted to and managed by information processing means. The information processing means uses edge computing devices such as Raspberry Pi and Intel NUC to save data to a recording device in real time. The saved data is analyzed by a machine learning model using a machine learning framework such as TensorFlow or PyTorch to predict usage trends.

[0285] The server uses a machine learning model that has learned past usage patterns to predict future usage scenarios and generate optimal usage guidance. This usage guidance is notified to users' smartphones or tablets via a transmission device. Devices equipped with communication functions such as Wi-Fi and Bluetooth are used for the transmission device.

[0286] As a specific example, if a meeting room is suddenly needed at a certain time, the system can quickly detect available rooms and present the optimal candidates to the user. The user can receive this guidance through a smartphone app and immediately reserve a room. It is also possible to predict and notify the user of the next available room or time.

[0287] Examples of prompt sentences include "Please tell me the reservation status and availability of each current meeting room." and "Where is the next available meeting room?" This allows the generative AI model to analyze the usage situation and provide an optimal answer.

[0288] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The terminal collects usage data from detection devices (temperature sensors, human presence sensors, etc.) installed in the factory. This data includes the number of people in the room, temperature, usage frequency, etc. The terminal measures this regularly and converts it into a packet format for storage. This prepares the collected data for transmission to the information processing means.

[0291] Step 2:

[0292] The server receives the usage data transmitted from the terminal. The received data is stored in the database using information processing means. For data storage, an SQL database or a NoSQL database is used. This records the usage data for subsequent analysis processing.

[0293] Step 3:

[0294] The server inputs data stored in the database into a machine learning model. Using the machine learning model (which uses TensorFlow or PyTorch), it learns past usage patterns and predicts future usage. This outputs predictive data to generate optimal usage guidance.

[0295] Step 4:

[0296] The server analyzes the generated usage instructions and creates an appropriate notification for the user. This notification includes the next available meeting room and the optimal usage time. The generated notification is converted to text format and passed to the sending device. This completes the preparation for the notification to the user.

[0297] Step 5:

[0298] The transmitting device sends notification information received from the server to the user's smartphone or tablet. Notifications are delivered in real time via Bluetooth or Wi-Fi. Users receive these notifications and use them to book meeting rooms or adjust their schedules. As a result, users can make rational decisions based on the guidance from the system.

[0299] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0300] This invention incorporates a function to recognize user emotions into a system that supports efficient resource management and smooth communication in an office environment. This system accurately grasps the usage status of meeting rooms and the emotional state of users, and makes optimal suggestions based on that information.

[0301] Device configuration

[0302] This system consists of a sensor device, an information processing device, an artificial intelligence model, a communication device, and an emotion engine in addition. The sensor device collects physical data such as the temperature and number of people in the meeting room. The emotion engine is used to analyze the user's voice data and recognize the user's emotional state.

[0303] Operating principle

[0304] The terminal grasps the physical state of the environment through a plurality of sensors arranged in the meeting room, and at the same time inputs the user's voice into the emotion engine. This emotion engine analyzes the voice data in real time and judges the user's emotional state.

[0305] The server receives data from the sensor device and the emotion engine, and stores it in the database using the information processing device. Based on this data, the artificial intelligence model makes predictions regarding the usage status of the meeting room and the user's emotional state.

[0306] Based on the prediction results, the server generates reservation guidance for the meeting room and notification content according to the emotional state. For example, when it is determined that the user is feeling stressed, it uses a warm notification message or proposes a schedule with not too many meetings.

[0307] The user can receive the reservation information and proposals for the meeting room proposed via a mobile terminal or a computer. Also, the user can accept proposals for communication means corresponding to the emotional state, enabling the construction of a more comfortable meeting environment.

[0308] Specific example

[0309] For example, if the user uses voice input to ask a question during a meeting and the emotion engine detects the user's stress from the voice, the server checks the user's current meeting schedule and proposes to adjust the next schedule to be more relaxed. Also, it is possible to devise ways to reduce the user's psychological burden by sending reminders for relaxation.

[0310] In this way, the present invention, by incorporating emotion recognition technology, provides a new form of office support system that more subtly and comprehensively improves communication and resource management in the office environment and takes into account the psychological well-being of users.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The terminal uses sensors installed in the conference room to acquire real-time data such as room temperature, humidity, and the number of people. This data is collected at regular intervals and sent to a server.

[0314] Step 2:

[0315] The device inputs the user's voice data into the emotion engine and performs voice analysis. The emotion engine generates analysis results and identifies the user's emotional state.

[0316] Step 3:

[0317] The server collects all data received from sensors and the emotion engine and stores it in a database. During this process, the data integrity is verified and converted into the format necessary for analysis.

[0318] Step 4:

[0319] The server analyzes the collected data using an artificial intelligence model. This analysis predicts the usage of meeting rooms and determines appropriate responses based on the users' emotional states.

[0320] Step 5:

[0321] Based on the analysis results, the server generates optimal meeting room schedules and suggestions that take into account the user's emotional state. For example, it can create schedule adjustments to reduce the user's burden and notifications that include positive messages.

[0322] Step 6:

[0323] Users receive suggested information via mobile apps or PCs. Based on this, users can adjust meeting room reservations and consider other suggestions for emotional support.

[0324] Step 7:

[0325] The terminal continuously supplies audio data to the emotion engine even while the meeting is in progress. If the user's emotions change, the system can make dynamic adjustments accordingly.

[0326] Thus, this system can grasp the user's emotional state in real time and provide optimal meeting room usage guidance and flexible scheduling suggestions accordingly.

[0327] (Example 2)

[0328] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0329] In modern office environments, insufficient utilization of meeting rooms and adequate consideration of users' emotional well-being are leading to problems such as stress and decreased productivity. To address this issue, a system is needed that integrates and analyzes environmental information and emotional states to provide appropriate recommendations.

[0330] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0331] In this invention, the server includes a detection device for measuring environmental state data, an emotion analysis device for analyzing voice data and determining the user's emotional state, and an information processing device for integrating the data and storing it in a memory area. This enables the automation of optimal meeting room usage suggestions and emotional care based on environmental and emotional information.

[0332] "Environmental condition data" refers to information that indicates physical conditions such as temperature, humidity, and the number of people in a workspace, such as an office.

[0333] A "detection device" is a device that includes sensors used to acquire environmental condition data.

[0334] "Voice data" refers to the waveform information of the sounds spoken by the user, and is data that is subject to emotion analysis in real time.

[0335] An "emotion analysis device" is a combination of hardware and software that analyzes voice data to determine the user's emotional state.

[0336] "Memory area" refers to a database or storage area where acquired data is stored and managed for later analysis and use.

[0337] An "information processing device" is a computer system that integrates and stores data collected from detection devices and emotion analysis devices.

[0338] A "generative AI model" is an artificial intelligence algorithm that uses stored data for analysis and automatically generates optimal suggestions.

[0339] "Communication methods" refer to technologies such as the internet and wireless communication used to deliver suggestions and notifications generated by a server to users.

[0340] "Suggestions" refer to information that includes improvement measures and recommended actions provided to users based on the analysis results.

[0341] This invention is a system aimed at efficient resource management and improved communication in an office environment, accurately understanding user emotions and making optimal suggestions based on that information. The main components of the system include a detection device that measures the physical state of the environment, an emotion analysis device that analyzes voice data, and an information processing device that integrates and manages the data.

[0342] The server uses sensing devices to measure environmental conditions. This allows for the real-time collection of data such as temperature, humidity, and the number of people in the room. Simultaneously, it uses an emotion analysis device to analyze the user's voice. The emotion analysis device processes the voice data and recognizes the user's emotional state in real time. This process utilizes speech recognition technology and natural language processing technology.

[0343] The collected data is integrated into an information processing unit on the server and stored in its memory. The information processing unit utilizes a generative AI model to analyze this data and generate optimal suggestions. This model has the ability to predict future usage patterns and emotional states by learning from past data.

[0344] The suggested content will be notified to the user via communication means. Users can receive these suggestions via mobile devices or computers, and use them to help adjust meetings and manage their emotional state. For example, if emotion analysis determines that the user is experiencing stress, it will be recommended to suggest a more relaxing schedule for the next meeting or to send a warm message.

[0345] As a concrete example, one could input a prompt message such as, "When a user is feeling stressed during a meeting, how should we suggest ways to reduce their psychological burden?" This would allow the system to provide appropriate feedback and improve the quality of communication in the office environment.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The terminal receives environmental data from sensors in the conference room. This data includes temperature, humidity, and the number of people in the room. The terminal converts this raw data into digital data and prepares it for transmission to the server. At this point, the input is analog data from the sensors, and the output is digital data that can be sent to the server.

[0349] Step 2:

[0350] The terminal inputs the user's voice data into an emotion analysis device. This input is received as an audio signal and processed by the emotion analysis device. This device uses speech recognition technology to measure the characteristics of the voice and extract features to identify the user's emotions. The output here is attribute data indicating the user's emotional state.

[0351] Step 3:

[0352] The server receives environmental state data and sentiment analysis data transmitted from the terminal. The received data is integrated by an information processing device, organized, and stored in memory. This operation involves integrating data in different formats and assigning timestamps. The integrated data is then used for subsequent analysis.

[0353] Step 4:

[0354] The server performs analysis based on a generative AI model using integrated data. This model learns from past usage patterns and sentiment data, and has the ability to predict future usage and sentiment. Based on the input data, the model generates optimal suggestions and prepares them for use in the next step. The output is the suggestions presented to the user.

[0355] Step 5:

[0356] The server notifies the user of the generated suggestions via communication channels. Suggestions, including scheduling adjustments and consideration of emotional state, are created and sent via mobile devices or computers. The user receives specific action instructions and warm, supportive response messages from the system.

[0357] (Application Example 2)

[0358] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0359] In traditional brick-and-mortar stores, providing services that take into account customers' emotional states was difficult, and there was a lack of concrete measures to improve customer satisfaction. Therefore, there was a need for a system that could analyze customers' emotional states in real time and provide appropriate services and suggestions based on that analysis.

[0360] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0361] In this invention, the server includes a detection device means for acquiring usage data and voice data, an information processing device means for storing the data received from the detection device means, and an artificial intelligence model means for analyzing the data stored in the information processing device means and generating appropriate suggestions according to the emotional state based on the analysis results. This makes it possible to provide services in a physical store that are tailored to the emotional state of the customer.

[0362] "Usage data" refers to information about customer behavior and activities within physical stores, including customer visit frequency and length of stay.

[0363] "Voice data" refers to digital information recorded from customer conversations and statements, and is used for analyzing emotional states.

[0364] "Detection device means" refers to equipment or sensors placed within the store to acquire usage data and audio data.

[0365] An "information processing device" is a computer system that stores data received from a detection device and facilitates its analysis.

[0366] "Artificial intelligence model means" refers to a function that includes an algorithm that analyzes accumulated data and determines the emotional state of customers.

[0367] "Communication device means" refers to a communication device used to notify store staff or systems of generated proposals and service details.

[0368] The system implementing this invention evaluates customer behavior in physical stores and provides services based on customer emotions. The server collects usage data and voice data using detection devices installed in the store. The detection devices used include microphones for voice capture and sensors that track customer movements. This data is stored by an information processing device.

[0369] The information processing device has a program installed that processes the received data and performs data analysis using an artificial intelligence model. The artificial intelligence model analyzes the customer's emotions from the voice data and estimates their emotional state based on that analysis. The technologies used in this process include natural language processing and machine learning algorithms, which improve the accuracy of the analysis.

[0370] The analyzed data is transmitted by the server to a communication device, which notifies store staff in real time of service suggestions based on the customer's emotional state. The communication device functions as a display or mobile device for staff, showing them what kind of service is appropriate for the customer.

[0371] For example, if a customer's stress level is detected through their voice during a store visit, the server sends a notification to the staff saying, "Please suggest products that will help the customer relax." This allows the staff to provide customer-tailored service. An example of a prompt message to input to the AI ​​model would be, "Recognize the customer's emotions and, if they are feeling stressed, suggest what service prompts would be effective."

[0372] This will enable appropriate responses tailored to each customer's situation, significantly improving the quality of service at physical stores.

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The terminal acquires customer voice data through microphones and sensors installed in the store. The input is customer conversation and behavior, and the output is real-time digital voice data. This data undergoes initial processing such as noise reduction and time axis adjustment of the audio.

[0376] Step 2:

[0377] The server receives audio data transmitted from the terminal through an information processing device. The input is digital audio data from the terminal, and the output is stored in an audio database. During this process, the data is organized and associated metadata (date, time, location, etc.) is added.

[0378] Step 3:

[0379] The server analyzes the accumulated voice data using an artificial intelligence model. This model uses natural language processing and sentiment analysis algorithms to estimate the customer's emotional state from the input voice data. The output is the estimated emotion label (e.g., relaxed, stressed).

[0380] Step 4:

[0381] The server generates service suggestions corresponding to the emotional state based on the analysis results. The input is the emotional label obtained in step 3, and the output is service suggestions and prompt statements. This generation process includes searching for candidate suggestions from the database and optimizing them according to the individual situation.

[0382] Step 5:

[0383] The server transmits the service proposal generated through the transmission device to the store staff. The input is the service proposal generated in step 4, and the output is a notification displayed on the staff's display or mobile device. This notification includes specific customer service guidelines and product suggestions.

[0384] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0390] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0391] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0392] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0393] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0394] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0395] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0396] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0397] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0398] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0400] This invention provides a system to support efficient communication and resource management in an office. This system uses the following devices and methods to collect meeting room and occupancy information in real time and manage usage status.

[0401] Device configuration

[0402] The system consists of multiple sensor devices installed in the conference rooms, an information processing device for managing the data, an artificial intelligence model for analyzing the data, and a communication device for sending notifications. Each conference room is equipped with sensor devices such as temperature sensors and motion sensors to continuously acquire data on the room's usage.

[0403] Operating principle

[0404] Each conference room is equipped with a sensor device, which periodically collects information such as the number of people in the room, temperature, and humidity. This data is transmitted to an information processing unit via a communication protocol.

[0405] The server stores data received from sensors in a database and analyzes it using an artificial intelligence model. The AI ​​model learns from past data and patterns to predict future meeting room usage trends. Based on these predictions, it generates optimal meeting room reservations and alternative options for the user.

[0406] Users can view real-time information and book meeting rooms through interfaces provided via mobile applications or PCs. Furthermore, they can adjust their schedules based on suggestions from the system.

[0407] Specific example

[0408] For example, if a user tries to book a meeting room at 10 AM, the server checks existing booking information and detects that the time slot is already fully booked. In this case, the server uses an artificial intelligence model to suggest other available meeting rooms or nearby time slots to help the user make the best choice. Furthermore, if sensors detect overcrowding in a meeting room, the server notifies the user of a suggestion to reschedule the meeting, ensuring a more comfortable environment by avoiding overcrowding.

[0409] Thus, the present invention aims to achieve efficient management of meeting rooms and resources within an office, as well as smooth communication with users. This system contributes to improving office productivity by optimizing resources.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The terminal uses sensors installed in the conference room to acquire real-time data such as room temperature, people's movements, and usage status. The sensors collect data every 10 seconds and transmit it to the server via wireless communication.

[0413] Step 2:

[0414] The server receives the transmitted data and stores it in the database. During this process, the accuracy of the data is verified, and the data format is standardized as needed.

[0415] Step 3:

[0416] The server uses accumulated data to perform analysis using an artificial intelligence model. This analysis predicts booking trends for the following week and month based on past usage patterns of meeting rooms.

[0417] Step 4:

[0418] The server generates optimal meeting room usage recommendations based on the prediction results. For example, if congestion is predicted during a specific time slot, it will automatically suggest other available time slots or meeting rooms.

[0419] Step 5:

[0420] The server generates a notification and sends it to each user. Users receive this information via a mobile app or PC and adjust their schedules accordingly.

[0421] Step 6:

[0422] The system reserves meeting rooms based on the information provided by the user. Once the user confirms the reservation, the server updates the reservation status and manages the usage schedule in conjunction with the sensor system.

[0423] Step 7:

[0424] As the meeting start time approaches, the device re-checks the situation using sensors to detect whether the number of participants and room temperature are appropriate. If necessary, it automatically adjusts the environmental settings.

[0425] Step 8:

[0426] Users submit feedback via a mobile app after the meeting. The server receives this feedback and uses it to improve analysis results and predictive models.

[0427] (Example 1)

[0428] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0429] In office and other business spaces, there is a need to efficiently manage the usage of meeting rooms and equipment, optimizing utilization while avoiding waste and congestion. Traditional systems suffer from insufficient real-time capabilities and future usage forecasting, leading to issues such as excessive resource use and inappropriate bookings. Furthermore, a lack of appropriate and prompt notifications to users makes efficient time management difficult.

[0430] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0431] In this invention, the server includes a sensor means for acquiring usage status information, a processing means for storing the information received from the sensor means in a storage device, and an artificial intelligence means for analyzing the information stored in the processing means and generating optimal usage guidance based on the analysis results. This enables real-time monitoring of the usage status of meeting rooms and equipment, efficient reservation management, and prediction of future usage.

[0432] "Usage status information" refers to data regarding the current usage status and environmental conditions of meeting rooms and equipment.

[0433] "Sensor means" refers to devices or equipment installed to acquire usage information from the physical environment.

[0434] A "memory device" refers to a device or system that has the function of structuring and storing acquired usage information.

[0435] "Processing means" refers to a device or software that has the function of analyzing information stored in a memory device or communicating with other devices or systems.

[0436] "Artificial intelligence tools" are those that have the function of learning past usage patterns and executing algorithms and models to predict future usage.

[0437] "Communication means" refers to the network or devices used to notify users of the generated usage instructions.

[0438] "User guide" refers to information generated based on analysis and predictions, intended to present users with the optimal usage methods and alternative options.

[0439] This invention aims to efficiently manage meeting rooms and equipment in an office environment and to provide users with timely information. It mainly consists of sensor means, storage devices, processing means, artificial intelligence means, and communication means.

[0440] Each meeting room is equipped with motion sensors and temperature sensors, which act as terminals to periodically acquire information about the room's usage. This information is stored digitally in a storage device.

[0441] The server analyzes the data collected in the storage device. As a preprocessing step, it cleanses and normalizes the data, and uses a generative AI model to predict future usage trends from past data. For example, if usage is high on a particular day of the week or time slot, it has a function to warn users in advance that they should be careful when making reservations for the same time slot the following week.

[0442] Users receive this information via PCs or mobile devices. They can obtain usage instructions generated via communication in real time and make reservations based on the optimal usage instructions and alternatives provided by the system. This allows for efficient resource utilization while avoiding congestion.

[0443] For example, if a user enters the prompt "I would like to reserve a meeting room for 10:00 AM next Wednesday," the server will use an AI model to suggest alternative times and meeting rooms, as that time slot may be busy. In this way, the system achieves effective resource management and a smooth user experience.

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The terminal uses motion sensors and temperature sensors installed in the conference room to acquire information about the number of people in the room and the environment. It receives real-time physical data as input and converts it into a digital format. As output, it generates converted usage information and transmits it to the server via a communication protocol.

[0447] Step 2:

[0448] The server receives usage information sent from terminals. It takes data packets from each meeting room as input and saves that data to a database in a structured format as output. Specifically, it performs data validation and cleansing (removing unnecessary data and errors) to conform to the storage format.

[0449] Step 3:

[0450] The server begins data analysis based on the stored data. It uses cleansed historical usage information as input and employs a generative AI model for analysis. The output generates meeting room usage trends and future usage predictions. Specifically, it performs time-series analysis and pattern recognition to extract usage trends for specific days of the week and time slots.

[0451] Step 4:

[0452] The server generates user guidance based on the analysis results. It uses predictive data generated by an AI model as input and outputs optimal meeting room reservation guidance and alternative options. Specifically, it generates prompt messages to avoid time slots that are already booked and provides these to the user along with next-best options.

[0453] Step 5:

[0454] Users receive real-time usage information from the server via their PC or mobile device. Their input is receiving notifications from the server, and their output is confirming their meeting room reservation based on that information. Specifically, they complete the reservation by reviewing and selecting the options suggested on the user interface.

[0455] (Application Example 1)

[0456] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0457] The challenge lies in efficiently managing the usage of meeting rooms and break spaces within the factory and providing users with appropriate usage guidance to optimize resources and improve employee convenience. In particular, real-time information provision and the presentation of alternative solutions are required to respond to sudden changes and requests.

[0458] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0459] In this invention, the server includes a detection device means for acquiring usage status data, an information processing means for storing the data received from the detection device in a recording device, and a machine learning model means for analyzing the data stored in the information processing means and generating optimal usage guidance based on the analysis results. This makes it possible to optimize the usage status of meeting rooms and break spaces within a factory and provide users with quick and appropriate guidance.

[0460] "Usage data" refers to information about the use of meeting rooms and break spaces acquired by detection devices, and includes data such as the number of people, duration, temperature, and frequency of use.

[0461] A "detection device" refers to a sensor or device installed in meeting rooms or break areas to collect usage data.

[0462] "Information processing means" refers to a system or computer that has the function of storing data received from a detection device in a recording device and processing or calculating the data as needed.

[0463] A "recording device" is a storage medium or system that stores data acquired by information processing means and allows it to be retrieved as needed.

[0464] A "machine learning model" is an algorithm or program that utilizes artificial intelligence technology to learn from past data and predict future usage based on new data.

[0465] "Usage Guide" refers to information generated based on analysis results, which suggests the optimal way for users to utilize meeting rooms and break spaces.

[0466] A "transmitting device" is a device or system that has a communication function to notify users of the generated usage instructions.

[0467] A "predictive model" is a model that uses statistical or machine learning methods to predict future usage of meeting rooms and break spaces based on past usage patterns.

[0468] To implement this invention, it is necessary to build a system that collects usage data by installing multiple detection devices in meeting rooms and break areas within a factory. Suitable detection devices include temperature sensors and motion sensors. This data is transmitted to and managed by an information processing system. The information processing system uses edge computing devices such as Raspberry Pi and Intel NUC to save the data to a recording device in real time. The saved data is analyzed by machine learning models using machine learning frameworks such as TensorFlow and PyTorch to predict usage trends.

[0469] The server uses a machine learning model that has learned past usage patterns to predict future usage and generate optimal usage guidance. This guidance is sent to the user's smartphone or tablet via a transmission device. The transmission device uses a device equipped with communication capabilities such as Wi-Fi or Bluetooth.

[0470] For example, if a meeting room is urgently needed at a certain time, the system quickly detects available rooms and presents the user with the best option. The user receives this notification via a smartphone app and can immediately book a room. It can also predict and notify the user of the next available room and time.

[0471] Examples of prompts include, "Please tell me the current reservation status and availability of each meeting room," or "Which meeting room is available next?" This allows the generative AI model to analyze the usage situation and provide the most appropriate answer.

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] The terminal collects usage data from detection devices (temperature sensors and motion sensors) installed within the factory. This data includes the number of people in the room, the temperature, and the frequency of use. The terminal periodically measures this data and converts it into a packet format for storage. This prepares the collected data for transmission to the information processing system.

[0475] Step 2:

[0476] The server receives usage data sent from the terminal. The received data is stored in a database using information processing tools. SQL databases or NoSQL databases are used for data storage. This ensures that the usage data is recorded for subsequent analysis.

[0477] Step 3:

[0478] The server inputs data stored in the database into a machine learning model. Using the machine learning model (which uses TensorFlow or PyTorch), it learns past usage patterns and predicts future usage. This outputs predictive data to generate optimal usage guidance.

[0479] Step 4:

[0480] The server analyzes the generated usage instructions and creates an appropriate notification for the user. This notification includes the next available meeting room and the optimal usage time. The generated notification is converted to text format and passed to the sending device. This completes the preparation for the notification to the user.

[0481] Step 5:

[0482] The transmitting device sends notification information received from the server to the user's smartphone or tablet. Notifications are delivered in real time via Bluetooth or Wi-Fi. Users receive these notifications and use them to book meeting rooms or adjust their schedules. As a result, users can make rational decisions based on the guidance from the system.

[0483] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0484] This invention incorporates a function to recognize user emotions into a system that supports efficient resource management and smooth communication in an office environment. This system accurately grasps the usage status of meeting rooms and the emotional state of users, and makes optimal suggestions based on that information.

[0485] Device configuration

[0486] This system consists of a sensor device, an information processing device, an artificial intelligence model, a communication device, and an emotion engine. The sensor device collects physical data such as the temperature and number of people in the meeting room. The emotion engine is used to analyze the user's voice data and recognize the user's emotional state.

[0487] Operating principle

[0488] The terminal uses multiple sensors placed in the conference room to grasp the physical state of the environment, while simultaneously inputting the user's voice into an emotion engine. This emotion engine analyzes the voice data in real time to determine the user's emotional state.

[0489] The server receives data from sensor devices and an emotion engine and stores it in a database using an information processing device. Based on this data, an artificial intelligence model makes predictions about the usage status of meeting rooms and the emotional state of users.

[0490] Based on the prediction results, the server generates meeting room reservation information and notifications tailored to the user's emotional state. For example, if the server determines that the user is stressed, it will use a warm and friendly notification message and suggest a schedule with fewer meetings.

[0491] Users can receive suggested meeting room reservation information and recommendations via mobile devices or computers. They can also accept suggestions for communication methods that cater to their emotional state, enabling the creation of a more comfortable meeting environment.

[0492] Specific example

[0493] For example, if a user asks a question using voice input during a meeting, and the emotion engine detects the user's stress from the audio, the server will check the user's current meeting schedule and suggest adjusting the next meeting to a more relaxed one. It is also possible to reduce the user's psychological burden by sending relaxation reminders.

[0494] In this way, the present invention, by incorporating emotion recognition technology, provides a new form of office support system that more subtly and comprehensively improves communication and resource management in the office environment and takes into account the psychological well-being of users.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The terminal uses sensors installed in the conference room to acquire real-time data such as room temperature, humidity, and the number of people. This data is collected at regular intervals and sent to a server.

[0498] Step 2:

[0499] The device inputs the user's voice data into the emotion engine and performs voice analysis. The emotion engine generates analysis results and identifies the user's emotional state.

[0500] Step 3:

[0501] The server collects all data received from sensors and the emotion engine and stores it in a database. During this process, the data integrity is verified and converted into the format necessary for analysis.

[0502] Step 4:

[0503] The server analyzes the collected data using an artificial intelligence model. This analysis predicts the usage of meeting rooms and determines appropriate responses based on the users' emotional states.

[0504] Step 5:

[0505] Based on the analysis results, the server generates optimal meeting room schedules and suggestions that take into account the user's emotional state. For example, it can create schedule adjustments to reduce the user's burden and notifications that include positive messages.

[0506] Step 6:

[0507] Users receive suggested information via mobile apps or PCs. Based on this, users can adjust meeting room reservations and consider other suggestions for emotional support.

[0508] Step 7:

[0509] The terminal continuously supplies audio data to the emotion engine even while the meeting is in progress. If the user's emotions change, the system can make dynamic adjustments accordingly.

[0510] Thus, this system can grasp the user's emotional state in real time and provide optimal meeting room usage guidance and flexible scheduling suggestions accordingly.

[0511] (Example 2)

[0512] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0513] In modern office environments, insufficient utilization of meeting rooms and adequate consideration of users' emotional well-being are leading to problems such as stress and decreased productivity. To address this issue, a system is needed that integrates and analyzes environmental information and emotional states to provide appropriate recommendations.

[0514] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0515] In this invention, the server includes a detection device for measuring environmental state data, an emotion analysis device for analyzing voice data and determining the user's emotional state, and an information processing device for integrating the data and storing it in a memory area. This enables the automation of optimal meeting room usage suggestions and emotional care based on environmental and emotional information.

[0516] "Environmental condition data" refers to information that indicates physical conditions such as temperature, humidity, and the number of people in a workspace, such as an office.

[0517] A "detection device" is a device that includes sensors used to acquire environmental condition data.

[0518] "Voice data" refers to the waveform information of the sounds spoken by the user, and is data that is subject to emotion analysis in real time.

[0519] An "emotion analysis device" is a combination of hardware and software that analyzes voice data to determine the user's emotional state.

[0520] "Memory area" refers to a database or storage area where acquired data is stored and managed for later analysis and use.

[0521] An "information processing device" is a computer system that integrates and stores data collected from detection devices and emotion analysis devices.

[0522] A "generative AI model" is an artificial intelligence algorithm that uses stored data for analysis and automatically generates optimal suggestions.

[0523] "Communication methods" refer to technologies such as the internet and wireless communication used to deliver suggestions and notifications generated by a server to users.

[0524] "Suggestions" refer to information that includes improvement measures and recommended actions provided to users based on the analysis results.

[0525] This invention is a system aimed at efficient resource management and improved communication in an office environment, accurately understanding user emotions and making optimal suggestions based on that information. The main components of the system include a detection device that measures the physical state of the environment, an emotion analysis device that analyzes voice data, and an information processing device that integrates and manages the data.

[0526] The server uses sensing devices to measure environmental conditions. This allows for the real-time collection of data such as temperature, humidity, and the number of people in the room. Simultaneously, it uses an emotion analysis device to analyze the user's voice. The emotion analysis device processes the voice data and recognizes the user's emotional state in real time. This process utilizes speech recognition technology and natural language processing technology.

[0527] The collected data is integrated into an information processing unit on the server and stored in its memory. The information processing unit utilizes a generative AI model to analyze this data and generate optimal suggestions. This model has the ability to predict future usage patterns and emotional states by learning from past data.

[0528] The suggested content will be notified to the user via communication means. Users can receive these suggestions via mobile devices or computers, and use them to help adjust meetings and manage their emotional state. For example, if emotion analysis determines that the user is experiencing stress, it will be recommended to suggest a more relaxing schedule for the next meeting or to send a warm message.

[0529] As a concrete example, one could input a prompt message such as, "When a user is feeling stressed during a meeting, how should we suggest ways to reduce their psychological burden?" This would allow the system to provide appropriate feedback and improve the quality of communication in the office environment.

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] The terminal receives environmental data from sensors in the conference room. This data includes temperature, humidity, and the number of people in the room. The terminal converts this raw data into digital data and prepares it for transmission to the server. At this point, the input is analog data from the sensors, and the output is digital data that can be sent to the server.

[0533] Step 2:

[0534] The terminal inputs the user's voice data into an emotion analysis device. This input is received as an audio signal and processed by the emotion analysis device. This device uses speech recognition technology to measure the characteristics of the voice and extract features to identify the user's emotions. The output here is attribute data indicating the user's emotional state.

[0535] Step 3:

[0536] The server receives environmental state data and sentiment analysis data transmitted from the terminal. The received data is integrated by an information processing device, organized, and stored in memory. This operation involves integrating data in different formats and assigning timestamps. The integrated data is then used for subsequent analysis.

[0537] Step 4:

[0538] The server performs analysis based on a generative AI model using integrated data. This model learns from past usage patterns and sentiment data, and has the ability to predict future usage and sentiment. Based on the input data, the model generates optimal suggestions and prepares them for use in the next step. The output is the suggestions presented to the user.

[0539] Step 5:

[0540] The server notifies the user of the generated suggestions via communication channels. Suggestions, including scheduling adjustments and consideration of emotional state, are created and sent via mobile devices or computers. The user receives specific action instructions and warm, supportive response messages from the system.

[0541] (Application Example 2)

[0542] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0543] In traditional brick-and-mortar stores, providing services that take into account customers' emotional states was difficult, and there was a lack of concrete measures to improve customer satisfaction. Therefore, there was a need for a system that could analyze customers' emotional states in real time and provide appropriate services and suggestions based on that analysis.

[0544] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0545] In this invention, the server includes a detection device means for acquiring usage data and voice data, an information processing device means for storing the data received from the detection device means, and an artificial intelligence model means for analyzing the data stored in the information processing device means and generating appropriate suggestions according to the emotional state based on the analysis results. This makes it possible to provide services in a physical store that are tailored to the emotional state of the customer.

[0546] "Usage data" refers to information about customer behavior and activities within physical stores, including customer visit frequency and length of stay.

[0547] "Voice data" refers to digital information recorded from customer conversations and statements, and is used for analyzing emotional states.

[0548] "Detection device means" refers to equipment or sensors placed within the store to acquire usage data and audio data.

[0549] An "information processing device" is a computer system that stores data received from a detection device and facilitates its analysis.

[0550] "Artificial intelligence model means" refers to a function that includes an algorithm that analyzes accumulated data and determines the emotional state of customers.

[0551] "Communication device means" refers to a communication device used to notify store staff or systems of generated proposals and service details.

[0552] The system implementing this invention evaluates customer behavior in physical stores and provides services based on customer emotions. The server collects usage data and voice data using detection devices installed in the store. The detection devices used include microphones for voice capture and sensors that track customer movements. This data is stored by an information processing device.

[0553] The information processing device has a program installed that processes the received data and performs data analysis using an artificial intelligence model. The artificial intelligence model analyzes the customer's emotions from the voice data and estimates their emotional state based on that analysis. The technologies used in this process include natural language processing and machine learning algorithms, which improve the accuracy of the analysis.

[0554] The analyzed data is transmitted by the server to a communication device, which notifies store staff in real time of service suggestions based on the customer's emotional state. The communication device functions as a display or mobile device for staff, showing them what kind of service is appropriate for the customer.

[0555] For example, if a customer's stress level is detected through their voice during a store visit, the server sends a notification to the staff saying, "Please suggest products that will help the customer relax." This allows the staff to provide customer-tailored service. An example of a prompt message to input to the AI ​​model would be, "Recognize the customer's emotions and, if they are feeling stressed, suggest what service prompts would be effective."

[0556] This will enable appropriate responses tailored to each customer's situation, significantly improving the quality of service at physical stores.

[0557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0558] Step 1:

[0559] The terminal acquires customer voice data through microphones and sensors installed in the store. The input is customer conversations and actions, and the output is real-time digital voice data. This data undergoes initial processing such as noise reduction and time axis adjustment of the audio.

[0560] Step 2:

[0561] The server receives audio data transmitted from the terminal through an information processing device. The input is digital audio data from the terminal, and the output is stored in an audio database. In this process, the data is organized and associated metadata (date, time, location, etc.) is added.

[0562] Step 3:

[0563] The server analyzes the accumulated voice data using an artificial intelligence model. This model uses natural language processing and sentiment analysis algorithms to estimate the customer's emotional state from the input voice data. The output is the estimated emotion label (e.g., relaxed, stressed).

[0564] Step 4:

[0565] The server generates service suggestions corresponding to the emotional state based on the analysis results. The input is the emotional label obtained in step 3, and the output is service suggestions and prompt statements. This generation process includes searching for candidate suggestions from the database and optimizing them according to the individual situation.

[0566] Step 5:

[0567] The server transmits the service proposal generated through the transmission device to the store staff. The input is the service proposal generated in step 4, and the output is a notification displayed on the staff's display or mobile device. This notification includes specific customer service guidelines and product suggestions.

[0568] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0569] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0570] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0571] [Fourth Embodiment]

[0572] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0573] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0574] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0575] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0576] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0577] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0578] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0579] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0580] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0581] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0582] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0583] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0584] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0585] This invention provides a system to support efficient communication and resource management in an office. This system uses the following devices and methods to collect meeting room and occupancy information in real time and manage usage status.

[0586] Device configuration

[0587] The system consists of multiple sensor devices installed in the conference rooms, an information processing device for managing the data, an artificial intelligence model for analyzing the data, and a communication device for sending notifications. Each conference room is equipped with sensor devices such as temperature sensors and motion sensors to continuously acquire data on the room's usage.

[0588] Operating principle

[0589] Each conference room is equipped with a sensor device, which periodically collects information such as the number of people in the room, temperature, and humidity. This data is transmitted to an information processing unit via a communication protocol.

[0590] The server stores data received from sensors in a database and analyzes it using an artificial intelligence model. The AI ​​model learns from past data and patterns to predict future meeting room usage trends. Based on these predictions, it generates optimal meeting room reservations and alternative options for the user.

[0591] Users can view real-time information and book meeting rooms through interfaces provided via mobile applications or PCs. Furthermore, they can adjust their schedules based on suggestions from the system.

[0592] Specific example

[0593] For example, if a user tries to book a meeting room at 10 AM, the server checks existing booking information and detects that the time slot is already fully booked. In this case, the server uses an artificial intelligence model to suggest other available meeting rooms or nearby time slots to help the user make the best choice. Furthermore, if sensors detect overcrowding in a meeting room, the server notifies the user of a suggestion to reschedule the meeting, ensuring a more comfortable environment by avoiding overcrowding.

[0594] Thus, the present invention aims to achieve efficient management of meeting rooms and resources within an office, as well as smooth communication with users. This system contributes to improving office productivity by optimizing resources.

[0595] The following describes the processing flow.

[0596] Step 1:

[0597] The terminal uses sensors installed in the conference room to acquire real-time data such as room temperature, people's movements, and usage status. The sensors collect data every 10 seconds and transmit it to the server via wireless communication.

[0598] Step 2:

[0599] The server receives the transmitted data and stores it in the database. During this process, the accuracy of the data is verified, and the data format is standardized as needed.

[0600] Step 3:

[0601] The server uses accumulated data to perform analysis using an artificial intelligence model. This analysis predicts booking trends for the following week and month based on past usage patterns of meeting rooms.

[0602] Step 4:

[0603] The server generates optimal meeting room usage recommendations based on the prediction results. For example, if congestion is predicted during a specific time slot, it will automatically suggest other available time slots or meeting rooms.

[0604] Step 5:

[0605] The server generates a notification and sends it to each user. Users receive this information via a mobile app or PC and adjust their schedules accordingly.

[0606] Step 6:

[0607] The system reserves meeting rooms based on the information provided by the user. Once the user confirms the reservation, the server updates the reservation status and manages the usage schedule in conjunction with the sensor system.

[0608] Step 7:

[0609] As the meeting start time approaches, the device re-checks the situation using sensors to detect whether the number of participants and room temperature are appropriate. If necessary, it automatically adjusts the environmental settings.

[0610] Step 8:

[0611] Users submit feedback via a mobile app after the meeting. The server receives this feedback and uses it to improve analysis results and predictive models.

[0612] (Example 1)

[0613] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0614] In office and other business spaces, there is a need to efficiently manage the usage of meeting rooms and equipment, optimizing utilization while avoiding waste and congestion. Traditional systems suffer from insufficient real-time capabilities and future usage forecasting, leading to issues such as excessive resource use and inappropriate bookings. Furthermore, a lack of appropriate and prompt notifications to users makes efficient time management difficult.

[0615] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0616] In this invention, the server includes a sensor means for acquiring usage status information, a processing means for storing the information received from the sensor means in a storage device, and an artificial intelligence means for analyzing the information stored in the processing means and generating optimal usage guidance based on the analysis results. This enables real-time monitoring of the usage status of meeting rooms and equipment, efficient reservation management, and prediction of future usage.

[0617] "Usage status information" refers to data regarding the current usage status and environmental conditions of meeting rooms and equipment.

[0618] "Sensor means" refers to devices or equipment installed to acquire usage information from the physical environment.

[0619] A "memory device" refers to a device or system that has the function of structuring and storing acquired usage information.

[0620] "Processing means" refers to a device or software that has the function of analyzing information stored in a memory device or communicating with other devices or systems.

[0621] "Artificial intelligence tools" are those that have the function of learning past usage patterns and executing algorithms and models to predict future usage.

[0622] "Communication means" refers to the network or devices used to notify users of the generated usage instructions.

[0623] "User guide" refers to information generated based on analysis and predictions, intended to present users with the optimal usage methods and alternative options.

[0624] This invention aims to efficiently manage meeting rooms and equipment in an office environment and to provide users with timely information. It mainly consists of sensor means, storage devices, processing means, artificial intelligence means, and communication means.

[0625] Each meeting room is equipped with motion sensors and temperature sensors, which act as terminals to periodically acquire information about the room's usage. This information is stored digitally in a storage device.

[0626] The server analyzes the data collected in the storage device. As a preprocessing step, it cleanses and normalizes the data, and uses a generative AI model to predict future usage trends from past data. For example, if usage is high on a particular day of the week or time slot, it has a function to warn users in advance that they should be careful when making reservations for the same time slot the following week.

[0627] Users receive this information via PCs or mobile devices. They can obtain usage instructions generated via communication in real time and make reservations based on the optimal usage instructions and alternatives provided by the system. This allows for efficient resource utilization while avoiding congestion.

[0628] For example, if a user enters the prompt "I would like to reserve a meeting room for 10:00 AM next Wednesday," the server will use an AI model to suggest alternative times and meeting rooms, as that time slot may be busy. In this way, the system achieves effective resource management and a smooth user experience.

[0629] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0630] Step 1:

[0631] The terminal uses motion sensors and temperature sensors installed in the conference room to acquire information about the number of people in the room and the environment. It receives real-time physical data as input and converts it into a digital format. As output, it generates converted usage information and transmits it to the server via a communication protocol.

[0632] Step 2:

[0633] The server receives usage information sent from terminals. It takes data packets from each meeting room as input and saves that data to a database in a structured format as output. Specifically, it performs data validation and cleansing (removing unnecessary data and errors) to conform to the storage format.

[0634] Step 3:

[0635] The server begins data analysis based on the stored data. It uses cleansed historical usage information as input and employs a generative AI model for analysis. The output generates meeting room usage trends and future usage predictions. Specifically, it performs time-series analysis and pattern recognition to extract usage trends for specific days of the week and time slots.

[0636] Step 4:

[0637] The server generates user guidance based on the analysis results. It uses predictive data generated by an AI model as input and outputs optimal meeting room reservation guidance and alternative options. Specifically, it generates prompt messages to avoid time slots that are already booked and provides these to the user along with next-best options.

[0638] Step 5:

[0639] Users receive real-time usage information from the server via their PC or mobile device. Their input is receiving notifications from the server, and their output is confirming their meeting room reservation based on that information. Specifically, they complete the reservation by reviewing and selecting the options suggested on the user interface.

[0640] (Application Example 1)

[0641] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0642] The challenge lies in efficiently managing the usage of meeting rooms and break spaces within the factory and providing users with appropriate usage guidance to optimize resources and improve employee convenience. In particular, real-time information provision and the presentation of alternative solutions are required to respond to sudden changes and requests.

[0643] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0644] In this invention, the server includes a detection device means for acquiring usage status data, an information processing means for storing the data received from the detection device in a recording device, and a machine learning model means for analyzing the data stored in the information processing means and generating optimal usage guidance based on the analysis results. This makes it possible to optimize the usage status of meeting rooms and break spaces within a factory and provide users with quick and appropriate guidance.

[0645] "Usage data" refers to information about the use of meeting rooms and break spaces acquired by detection devices, and includes data such as the number of people, duration, temperature, and frequency of use.

[0646] A "detection device" refers to a sensor or device installed in meeting rooms or break areas to collect usage data.

[0647] "Information processing means" refers to a system or computer that has the function of storing data received from a detection device in a recording device and processing or calculating the data as needed.

[0648] A "recording device" is a storage medium or system that stores data acquired by information processing means and allows it to be retrieved as needed.

[0649] A "machine learning model" is an algorithm or program that utilizes artificial intelligence technology to learn from past data and predict future usage based on new data.

[0650] "Usage Guide" refers to information generated based on analysis results, which suggests the optimal way for users to utilize meeting rooms and break spaces.

[0651] A "transmitting device" is a device or system that has a communication function to notify users of the generated usage instructions.

[0652] A "predictive model" is a model that uses statistical or machine learning methods to predict future usage of meeting rooms and break spaces based on past usage patterns.

[0653] To implement this invention, it is necessary to build a system that collects usage data by installing multiple detection devices in meeting rooms and break areas within a factory. Suitable detection devices include temperature sensors and motion sensors. This data is transmitted to and managed by an information processing system. The information processing system uses edge computing devices such as Raspberry Pi and Intel NUC to save the data to a recording device in real time. The saved data is analyzed by machine learning models using machine learning frameworks such as TensorFlow and PyTorch to predict usage trends.

[0654] The server uses a machine learning model that has learned past usage patterns to predict future usage and generate optimal usage guidance. This guidance is sent to the user's smartphone or tablet via a transmission device. The transmission device uses a device equipped with communication capabilities such as Wi-Fi or Bluetooth.

[0655] For example, if a meeting room is urgently needed at a certain time, the system quickly detects available rooms and presents the user with the best option. The user receives this notification via a smartphone app and can immediately book a room. It can also predict and notify the user of the next available room and time.

[0656] Examples of prompts include, "Please tell me the current reservation status and availability of each meeting room," or "Which meeting room is available next?" This allows the generative AI model to analyze the usage situation and provide the most appropriate answer.

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] The terminal collects usage data from detection devices (temperature sensors and motion sensors) installed within the factory. This data includes the number of people in the room, the temperature, and the frequency of use. The terminal periodically measures this data and converts it into a packet format for storage. This prepares the collected data for transmission to the information processing system.

[0660] Step 2:

[0661] The server receives usage data sent from the terminal. The received data is stored in a database using information processing tools. SQL databases or NoSQL databases are used for data storage. This ensures that the usage data is recorded for subsequent analysis.

[0662] Step 3:

[0663] The server inputs data stored in the database into a machine learning model. Using the machine learning model (which uses TensorFlow or PyTorch), it learns past usage patterns and predicts future usage. This outputs predictive data to generate optimal usage guidance.

[0664] Step 4:

[0665] The server analyzes the generated usage instructions and creates an appropriate notification for the user. This notification includes the next available meeting room and the optimal usage time. The generated notification is converted to text format and passed to the sending device. This completes the preparation for the notification to the user.

[0666] Step 5:

[0667] The transmitting device sends notification information received from the server to the user's smartphone or tablet. Notifications are delivered in real time via Bluetooth or Wi-Fi. Users receive these notifications and use them to book meeting rooms or adjust their schedules. As a result, users can make rational decisions based on the guidance from the system.

[0668] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0669] This invention incorporates a function to recognize user emotions into a system that supports efficient resource management and smooth communication in an office environment. This system accurately grasps the usage status of meeting rooms and the emotional state of users, and makes optimal suggestions based on that information.

[0670] Device configuration

[0671] This system consists of a sensor device, an information processing device, an artificial intelligence model, a communication device, and an emotion engine. The sensor device collects physical data such as the temperature and number of people in the meeting room. The emotion engine is used to analyze the user's voice data and recognize the user's emotional state.

[0672] Operating principle

[0673] The terminal uses multiple sensors placed in the conference room to grasp the physical state of the environment, while simultaneously inputting the user's voice into an emotion engine. This emotion engine analyzes the voice data in real time to determine the user's emotional state.

[0674] The server receives data from sensor devices and an emotion engine and stores it in a database using an information processing device. Based on this data, an artificial intelligence model makes predictions about the usage status of meeting rooms and the emotional state of users.

[0675] Based on the prediction results, the server generates meeting room reservation information and notifications tailored to the user's emotional state. For example, if the server determines that the user is stressed, it will use a warm and friendly notification message and suggest a schedule with fewer meetings.

[0676] Users can receive suggested meeting room reservation information and recommendations via mobile devices or computers. They can also accept suggestions for communication methods that cater to their emotional state, enabling the creation of a more comfortable meeting environment.

[0677] Specific example

[0678] For example, if a user asks a question using voice input during a meeting, and the emotion engine detects the user's stress from the audio, the server will check the user's current meeting schedule and suggest adjusting the next meeting to a more relaxed one. It is also possible to reduce the user's psychological burden by sending relaxation reminders.

[0679] In this way, the present invention, by incorporating emotion recognition technology, provides a new form of office support system that more subtly and comprehensively improves communication and resource management in the office environment and takes into account the psychological well-being of users.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The terminal uses sensors installed in the conference room to acquire real-time data such as room temperature, humidity, and the number of people. This data is collected at regular intervals and sent to a server.

[0683] Step 2:

[0684] The device inputs the user's voice data into the emotion engine and performs voice analysis. The emotion engine generates analysis results and identifies the user's emotional state.

[0685] Step 3:

[0686] The server collects all data received from sensors and the emotion engine and stores it in a database. During this process, the data integrity is verified and converted into the format necessary for analysis.

[0687] Step 4:

[0688] The server analyzes the collected data using an artificial intelligence model. This analysis predicts the usage of meeting rooms and determines appropriate responses based on the users' emotional states.

[0689] Step 5:

[0690] Based on the analysis results, the server generates optimal meeting room schedules and suggestions that take into account the user's emotional state. For example, it can create schedule adjustments to reduce the user's burden and notifications that include positive messages.

[0691] Step 6:

[0692] Users receive suggested information via mobile apps or PCs. Based on this, users can adjust meeting room reservations and consider other suggestions for emotional support.

[0693] Step 7:

[0694] The terminal continuously supplies audio data to the emotion engine even while the meeting is in progress. If the user's emotions change, the system can make dynamic adjustments accordingly.

[0695] Thus, this system can grasp the user's emotional state in real time and provide optimal meeting room usage guidance and flexible scheduling suggestions accordingly.

[0696] (Example 2)

[0697] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] In modern office environments, insufficient utilization of meeting rooms and adequate consideration of users' emotional well-being are leading to problems such as stress and decreased productivity. To address this issue, a system is needed that integrates and analyzes environmental information and emotional states to provide appropriate recommendations.

[0699] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0700] In this invention, the server includes a detection device for measuring environmental state data, an emotion analysis device for analyzing voice data and determining the user's emotional state, and an information processing device for integrating the data and storing it in a memory area. This enables the automation of optimal meeting room usage suggestions and emotional care based on environmental and emotional information.

[0701] "Environmental condition data" refers to information that indicates physical conditions such as temperature, humidity, and the number of people in a workspace, such as an office.

[0702] A "detection device" is a device that includes sensors used to acquire environmental condition data.

[0703] "Voice data" refers to the waveform information of the sounds spoken by the user, and is data that is subject to emotion analysis in real time.

[0704] An "emotion analysis device" is a combination of hardware and software that analyzes voice data to determine the user's emotional state.

[0705] "Memory area" refers to a database or storage area where acquired data is stored and managed for later analysis and use.

[0706] An "information processing device" is a computer system that integrates and stores data collected from detection devices and emotion analysis devices.

[0707] A "generative AI model" is an artificial intelligence algorithm that uses stored data for analysis and automatically generates optimal suggestions.

[0708] "Communication methods" refer to technologies such as the internet and wireless communication used to deliver suggestions and notifications generated by a server to users.

[0709] "Suggestions" refer to information that includes improvement measures and recommended actions provided to users based on the analysis results.

[0710] This invention is a system aimed at efficient resource management and improved communication in an office environment, accurately understanding user emotions and making optimal suggestions based on that information. The main components of the system include a detection device that measures the physical state of the environment, an emotion analysis device that analyzes voice data, and an information processing device that integrates and manages the data.

[0711] The server uses sensing devices to measure environmental conditions. This allows for the real-time collection of data such as temperature, humidity, and the number of people in the room. Simultaneously, it uses an emotion analysis device to analyze the user's voice. The emotion analysis device processes the voice data and recognizes the user's emotional state in real time. This process utilizes speech recognition technology and natural language processing technology.

[0712] The collected data is integrated into an information processing unit on the server and stored in its memory. The information processing unit utilizes a generative AI model to analyze this data and generate optimal suggestions. This model has the ability to predict future usage patterns and emotional states by learning from past data.

[0713] The suggested content will be notified to the user via communication means. Users can receive these suggestions via mobile devices or computers, and use them to help adjust meetings and manage their emotional state. For example, if emotion analysis determines that the user is experiencing stress, it will be recommended to suggest a more relaxing schedule for the next meeting or to send a warm message.

[0714] As a concrete example, one could input a prompt message such as, "When a user is feeling stressed during a meeting, how should we suggest ways to reduce their psychological burden?" This would allow the system to provide appropriate feedback and improve the quality of communication in the office environment.

[0715] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0716] Step 1:

[0717] The terminal receives environmental data from sensors in the conference room. This data includes temperature, humidity, and the number of people in the room. The terminal converts this raw data into digital data and prepares it for transmission to the server. At this point, the input is analog data from the sensors, and the output is digital data that can be sent to the server.

[0718] Step 2:

[0719] The terminal inputs the user's voice data into an emotion analysis device. This input is received as an audio signal and processed by the emotion analysis device. This device uses speech recognition technology to measure the characteristics of the voice and extract features to identify the user's emotions. The output here is attribute data indicating the user's emotional state.

[0720] Step 3:

[0721] The server receives environmental state data and sentiment analysis data transmitted from the terminal. The received data is integrated by an information processing device, organized, and stored in memory. This operation involves integrating data in different formats and assigning timestamps. The integrated data is then used for subsequent analysis.

[0722] Step 4:

[0723] The server performs analysis based on a generative AI model using integrated data. This model learns from past usage patterns and sentiment data, and has the ability to predict future usage and sentiment. Based on the input data, the model generates optimal suggestions and prepares them for use in the next step. The output is the suggestions presented to the user.

[0724] Step 5:

[0725] The server notifies the user of the generated suggestions via communication channels. Suggestions, including scheduling adjustments and consideration of emotional state, are created and sent via mobile devices or computers. The user receives specific action instructions and warm, supportive response messages from the system.

[0726] (Application Example 2)

[0727] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0728] In traditional brick-and-mortar stores, providing services that take into account customers' emotional states was difficult, and there was a lack of concrete measures to improve customer satisfaction. Therefore, there was a need for a system that could analyze customers' emotional states in real time and provide appropriate services and suggestions based on that analysis.

[0729] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0730] In this invention, the server includes a detection device means for acquiring usage data and voice data, an information processing device means for storing the data received from the detection device means, and an artificial intelligence model means for analyzing the data stored in the information processing device means and generating appropriate suggestions according to the emotional state based on the analysis results. This makes it possible to provide services in a physical store that are tailored to the emotional state of the customer.

[0731] "Usage data" refers to information about customer behavior and activities within physical stores, including customer visit frequency and length of stay.

[0732] "Voice data" refers to digital information recorded from customer conversations and statements, and is used for analyzing emotional states.

[0733] "Detection device means" refers to equipment or sensors placed within the store to acquire usage data and audio data.

[0734] An "information processing device" is a computer system that stores data received from a detection device and facilitates its analysis.

[0735] "Artificial intelligence model means" refers to a function that includes an algorithm that analyzes accumulated data and determines the emotional state of customers.

[0736] "Communication device means" refers to a communication device used to notify store staff or systems of generated proposals and service details.

[0737] The system implementing this invention evaluates customer behavior in physical stores and provides services based on customer emotions. The server collects usage data and voice data using detection devices installed in the store. The detection devices used include microphones for voice capture and sensors that track customer movements. This data is stored by an information processing device.

[0738] The information processing device has a program installed that processes the received data and performs data analysis using an artificial intelligence model. The artificial intelligence model analyzes the customer's emotions from the voice data and estimates their emotional state based on that analysis. The technologies used in this process include natural language processing and machine learning algorithms, which improve the accuracy of the analysis.

[0739] The analyzed data is transmitted by the server to a communication device, which notifies store staff in real time of service suggestions based on the customer's emotional state. The communication device functions as a display or mobile device for staff, showing them what kind of service is appropriate for the customer.

[0740] For example, if a customer's stress level is detected through their voice during a store visit, the server sends a notification to the staff saying, "Please suggest products that will help the customer relax." This allows the staff to provide customer-tailored service. An example of a prompt message to input to the AI ​​model would be, "Recognize the customer's emotions and, if they are feeling stressed, suggest what service prompts would be effective."

[0741] This will enable appropriate responses tailored to each customer's situation, significantly improving the quality of service at physical stores.

[0742] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0743] Step 1:

[0744] The terminal acquires customer voice data through microphones and sensors installed in the store. The input is customer conversations and actions, and the output is real-time digital voice data. This data undergoes initial processing such as noise reduction and time axis adjustment of the audio.

[0745] Step 2:

[0746] The server receives audio data transmitted from the terminal through an information processing device. The input is digital audio data from the terminal, and the output is stored in an audio database. In this process, the data is organized and associated metadata (date, time, location, etc.) is added.

[0747] Step 3:

[0748] The server analyzes the accumulated voice data using an artificial intelligence model. This model uses natural language processing and sentiment analysis algorithms to estimate the customer's emotional state from the input voice data. The output is the estimated emotion label (e.g., relaxed, stressed).

[0749] Step 4:

[0750] The server generates service suggestions corresponding to the emotional state based on the analysis results. The input is the emotional label obtained in step 3, and the output is service suggestions and prompt statements. This generation process includes searching for candidate suggestions from the database and optimizing them according to the individual situation.

[0751] Step 5:

[0752] The server transmits the service proposal generated through the transmission device to the store staff. The input is the service proposal generated in step 4, and the output is a notification displayed on the staff's display or mobile device. This notification includes specific customer service guidelines and product suggestions.

[0753] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0754] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0755] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0756] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0757] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0758] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0759] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0760] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0761] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0762] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0763] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0764] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0765] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0766] 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.

[0767] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0768] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0769] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0770] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0771] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0772] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0773] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0774] The following is further disclosed regarding the embodiments described above.

[0775] (Claim 1)

[0776] A sensor device for acquiring usage data,

[0777] An information processing device for storing data received from the aforementioned sensor device in a database,

[0778] An artificial intelligence model for analyzing data stored in the aforementioned information processing device and generating optimal user guidance based on the analysis results,

[0779] A communication device for notifying the user of the generated user guide,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, wherein the artificial intelligence model has the function of learning past usage patterns and predicting future usage.

[0783] (Claim 3)

[0784] The system according to claim 1, characterized in that the usage instructions provided suggest alternative available resources or schedules.

[0785] "Example 1"

[0786] (Claim 1)

[0787] A sensor means for acquiring usage status information,

[0788] Processing means for storing information received from the sensor means in a storage device,

[0789] An artificial intelligence means for analyzing the information stored in the processing means and generating optimal user guidance based on the analysis results,

[0790] A communication means for notifying the user of the generated usage instructions,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, wherein the artificial intelligence means has a function to learn past usage patterns and predict future usage.

[0794] (Claim 3)

[0795] The system according to claim 1, characterized in that the usage instructions provided suggest alternative available resources or schedules.

[0796] "Application Example 1"

[0797] (Claim 1)

[0798] A detection device for acquiring usage data,

[0799] Information processing means for storing data received from the detection device in a recording device,

[0800] A machine learning model for analyzing data stored in the aforementioned information processing means and generating optimal user guidance based on the analysis results,

[0801] A transmitting device for notifying the user of the generated usage instructions,

[0802] A predictive model for optimizing the usage of the aforementioned meeting rooms and break spaces,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, wherein the machine learning model has the function of learning past usage patterns and predicting future usage.

[0806] (Claim 3)

[0807] The system according to claim 1, characterized in that the usage instructions provided suggest alternative available resources or schedules.

[0808] "Example 2 of combining an emotion engine"

[0809] (Claim 1)

[0810] A detection device for measuring environmental condition data,

[0811] An emotion analysis device that analyzes voice data to determine the user's emotional state,

[0812] An information processing device for integrating data received from the aforementioned detection device and emotion analysis device and storing it in a memory area,

[0813] A generative AI model for analyzing data stored in the aforementioned information processing device and generating optimal suggestions based on the analysis results,

[0814] Means for notifying the user of the generated proposal via communication means,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, wherein the generating AI model has the function of learning past environmental and emotional patterns and predicting future usage and emotional states.

[0818] (Claim 3)

[0819] The system according to claim 1, characterized in that the notified suggestion recommends alternative resources or schedules that are appropriate to the user's emotional state.

[0820] "Application example 2 when combining with an emotional engine"

[0821] (Claim 1)

[0822] A detection device means for acquiring usage data and voice data,

[0823] Information processing means for storing data received from the aforementioned detection device means,

[0824] An artificial intelligence model means for analyzing data accumulated in the aforementioned information processing device and generating appropriate suggestions according to the emotional state based on the analysis results,

[0825] A communication device means for informing the user of the generated proposal,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the artificial intelligence model means has the function of learning past usage patterns and emotional information contained in voice data, and predicting suggestions according to future emotional states.

[0829] (Claim 3)

[0830] The system according to claim 1, characterized in that the notified proposal provides an appropriate service or schedule in accordance with alternative available resources or emotional state. [Explanation of symbols]

[0831] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensor device for acquiring usage data, An information processing device for storing data received from the aforementioned sensor device in a database, An artificial intelligence model for analyzing data stored in the aforementioned information processing device and generating optimal user guidance based on the analysis results, A communication device for notifying the user of the generated user guide, A system that includes this.

2. The system according to claim 1, wherein the artificial intelligence model has a function to learn past usage patterns and predict future usage.

3. The system according to claim 1, characterized in that the usage instructions provided suggest alternative available resources or schedules.

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