system

Abstract

The system according to the embodiment aims to automate management operations for condominiums and create an environment where elderly people can live in peace of mind. [Solution] The system according to the embodiment comprises a reception unit, a cleaning unit, a monitoring unit, and a reporting unit. The reception unit authenticates and notifies visitors. The cleaning unit cleans common areas. The monitoring unit monitors the status of equipment. The reporting unit analyzes data and creates reports.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has faced the problem of increasing management burdens due to the aging of condominium residents, making efficient management difficult.

[0005] The system according to the embodiment aims to automate management operations for condominiums and create an environment where elderly people can live in peace of mind. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a cleaning unit, a monitoring unit, and a reporting unit. The reception unit authenticates and notifies visitors. The cleaning unit cleans common areas. The monitoring unit monitors the status of equipment. The reporting unit analyzes data and creates reports. [Effects of the Invention]

[0007] The system according to the embodiment automates management tasks for condominiums and can create an environment where elderly people can live in peace of mind. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A condominium management system according to an embodiment of the present invention works with local governments to address the aging population problem in condominiums by utilizing robots and IoT devices to revolutionize condominium management. This condominium management system automates reception, cleaning, inspections, patrols, and reporting tasks, reducing the burden on management associations in condominiums with a large elderly population. Specifically, reception tasks are automated by a robot that receives visitors at the entrance, authenticates them using facial recognition technology, and notifies the residents of the visit. Cleaning tasks are automated by a robot vacuum cleaner that cleans common areas and operates according to a pre-set schedule. Inspection and patrol tasks are automated by an IoT device that constantly monitors the status of various facilities within the condominium and notifies the manager via a smartphone app if an abnormality occurs. Reporting tasks are automated by a generative AI that analyzes data collected from sensors, regularly creates reports, and sends them to the condominium association by email. This system reduces the burden on management associations in condominiums with a large elderly population and creates an environment where seniors can live with peace of mind. It also prevents depopulation and enables the establishment of a sustainable condominium management system. This will reduce the burden on management associations in apartment complexes with a large elderly population, and create an environment where the elderly can live with peace of mind. It will also help prevent marginalized communities and enable the creation of a sustainable management system for apartment complexes.

[0029] An apartment management system according to an embodiment includes a reception unit, a cleaning unit, a monitoring unit, and a reporting unit. The reception unit authenticates and notifies visitors. For example, the reception unit uses facial recognition technology to authenticate visitors and notifies the residents of the residence. The reception unit can also authenticate visitors using a two-dimensional code (e.g., a QR code (registered trademark)) or an ID card. For example, facial recognition technology uses deep learning to recognize the visitor's face and perform authentication. When a two-dimensional code or ID card is used, authentication is performed by the visitor scanning the two-dimensional code or presenting the ID card. The cleaning unit cleans common areas. For example, the cleaning unit cleans common areas using a robot vacuum cleaner. The robot vacuum cleaner operates according to a pre-set schedule to clean the common areas. For example, the robot vacuum cleaner has suction power, battery life, and sensor functions, allowing for efficient cleaning. The monitoring unit monitors the status of equipment. For example, the monitoring unit constantly monitors the status of equipment using sensors and notifies the manager if an abnormality occurs. The sensors may include temperature sensors, humidity sensors, and motion sensors. For example, a temperature sensor monitors the temperature of equipment and notifies the management association if an abnormal temperature change occurs. A humidity sensor monitors the humidity of equipment and notifies the management association if an abnormal humidity change occurs. A motion sensor monitors the operating status of equipment and notifies the management association if an abnormality occurs. The reporting unit analyzes the data and creates a report. The reporting unit analyzes the data collected from the sensors using, for example, a generation AI, periodically creates reports, and sends them to the management association. The generation AI analyzes the data using natural language generation and data analysis algorithms and creates reports. For example, the generation AI performs statistical analysis based on the data collected from the sensors and creates reports. The generation AI also analyzes the data using a data analysis algorithm and creates reports. As a result, the condominium management system according to the embodiment automates reception, cleaning, monitoring, and reporting tasks, thereby reducing the burden on management associations in condominiums with a large elderly population.

[0030] The reception unit can authenticate visitors using facial recognition technology and notify the residents of the location. Examples of facial recognition technology include deep learning and pattern matching. Deep learning is a technology that learns from large amounts of facial image data to recognize visitors' faces with high accuracy. For example, facial recognition technology using deep learning captures the visitor's face with a camera and performs authentication using a trained model. Pattern matching compares the visitor's facial image with a pre-registered facial image and performs authentication based on the degree of match. For example, facial recognition technology using pattern matching compares the visitor's facial image with facial images in a database and authenticates the visitor if the degree of match is high. This improves the accuracy of visitor authentication using facial recognition technology. Furthermore, the reception unit notifies the residents of the location of the visitor of information about the authenticated visitor. Notification methods include, for example, email, SMS, and app notification. For example, email notification is a method of sending visitor information to the resident's email address. SMS notification is a method of sending visitor information to the resident's mobile phone via SMS. App notification is a method of notifying residents of visitor information through a dedicated app, which allows for quick and accurate visitor authentication and notification.

[0031] The cleaning unit can clean common areas using a robot vacuum cleaner. The robot vacuum cleaner's characteristics include, for example, suction power, battery life, and sensor functions. Suction power is the ability of the robot vacuum cleaner to suck up dirt and dust and affects cleaning efficiency. For example, a robot vacuum cleaner with strong suction power can effectively suck up fine dirt and dust. Battery life indicates how long the robot vacuum cleaner can operate on a single charge. For example, a robot vacuum cleaner with a long battery life can clean a large area at once. Sensor functions enable the robot vacuum cleaner to detect and avoid obstacles. For example, a robot vacuum cleaner with sensor functions can clean efficiently without bumping into furniture or walls. This improves the efficiency of cleaning work by using a robot vacuum cleaner. Furthermore, the cleaning unit can operate the robot vacuum cleaner according to a pre-set schedule. For example, the cleaning unit can operate the robot vacuum cleaner at a set time every day to clean common areas. The cleaning unit can also operate the robot vacuum cleaner to clean after a specific event. For example, the cleaning department operates the robot vacuum cleaner to clean the common areas after an event is held in the apartment building, which allows the cleaning department to clean the common areas efficiently and effectively.

[0032] The monitoring unit constantly monitors the status of the equipment using sensors and can notify an administrator if an abnormality occurs. Examples of sensors include temperature sensors, humidity sensors, and operation sensors. The temperature sensor monitors the temperature of the equipment and notifies an administrator if an abnormal temperature change occurs. For example, the temperature sensor issues an alert if the temperature of the equipment exceeds a set range. The humidity sensor monitors the humidity of the equipment and notifies an administrator if an abnormal humidity change occurs. For example, the humidity sensor issues an alert if the humidity of the equipment exceeds a set range. The operation sensor monitors the operating status of the equipment and notifies an administrator if an abnormality occurs. For example, the operation sensor issues an alert if the equipment is not operating normally. This enables the use of sensors to quickly detect and respond to abnormalities in the equipment. Furthermore, the monitoring unit notifies an administrator if an abnormality occurs. Notification methods include, for example, email, SMS, and app notification. For example, email notification is a method of sending details of the abnormality to the administrator's email address. SMS notification is a method of sending details of the abnormality to the administrator's mobile phone via SMS. App notification is a method of notifying an administrator of details of the abnormality through a dedicated app. This allows the monitoring unit to quickly detect abnormalities in the equipment and notify the administrator.

[0033] The reporting department can use generative AI to analyze data collected from sensors, periodically create reports, and send them to the management association. Generative AI includes, for example, natural language generation and data analysis algorithms. Natural language generation is a technology that generates natural-sounding sentences based on data. For example, generative AI automatically creates reports based on data collected from sensors. Data analysis algorithms are a technology that analyzes data and extracts meaningful information. For example, generative AI analyzes data collected from sensors and detects outliers and trends. This automates the creation and transmission of reports, reducing the burden on the management association. Furthermore, the reporting department periodically creates reports and sends them to the management association. The contents of the reports include, for example, the status of equipment, occurrence of abnormalities, and cleaning implementation status. For example, the reporting department creates a report summarizing the status of equipment each month and sends it to the management association. In addition, the reporting department can also include details of any abnormalities in the report and send it to the management association. This allows the reporting department to quickly and accurately report the status of equipment and occurrence of abnormalities to the management association.

[0034] The reception unit can analyze past visit history and simplify the authentication process for frequent visitors. Methods for analyzing past visit history include, for example, databases and log analysis. A database is a system for storing visitor information and managing past visit history. For example, a database can be used to extract information about frequent visitors and simplify the authentication process. Log analysis is a technology for analyzing visitor access logs and identifying frequent visitors. For example, log analysis can be used to identify visitors who frequently visit during a specific time period and simplify the authentication process. This analysis of past visit history speeds up authentication of frequent visitors. Furthermore, the reception unit simplifies the authentication process for frequent visitors. For example, it can automatically recognize visitors who have visited frequently in the past and provide a simplified authentication process. It can also simplify the authentication process for visitors who visit during a specific time period. For example, it can register authentication information in advance for visitors who frequently visit during a specific time period and perform rapid authentication. This allows the reception unit to optimize the authentication process based on past visit history and improve convenience for visitors.

[0035] When authenticating a visitor, the reception unit can adjust the timing of notifications taking into account the schedule of the resident of the destination. Methods for acquiring the schedule of the resident of the destination include, for example, a calendar app or manual input. A calendar app is an application for managing the resident's schedule and can automatically acquire the resident's schedule. For example, the calendar app can be used to acquire the resident's meeting or outing plans and adjust the timing of notifications. Manual input is a method in which the resident manually inputs their own schedule. For example, the resident manually inputs their own schedule, and the reception unit can adjust the timing of notifications based on that information. This allows the timing of notifications to be adjusted according to the schedule of the resident of the destination. Furthermore, the reception unit adjusts the timing of notifications based on the resident's schedule. For example, if the resident is in a meeting, the reception unit can delay notifications. Also, if the resident is out, the reception unit can cancel notifications. Furthermore, if the resident is on a break, the reception unit can reduce notifications. This allows the timing of notifications to be adjusted according to the resident's schedule, improving convenience for the resident.

[0036] The cleaning unit can analyze past cleaning history and focus on cleaning areas that are particularly prone to getting dirty. Methods for analyzing past cleaning history include, for example, cleaning logs and databases. A cleaning log is data that records the status of cleaning implementation and is a system for managing past cleaning history. For example, a cleaning log can be used to identify areas that are particularly prone to getting dirty and focus on cleaning them. A database is a system for accumulating cleaning implementation status and managing past cleaning history. For example, a database can be used to identify areas that are particularly prone to getting dirty during specific time periods and focus on cleaning them. This enables efficient cleaning by analyzing past cleaning history. Furthermore, the cleaning unit can focus on cleaning areas that are particularly prone to getting dirty. For example, an area that is particularly prone to getting dirty can be identified from past cleaning history and focus on cleaning it. It can also focus on cleaning areas that are particularly prone to getting dirty during specific time periods. For example, an area that is particularly prone to getting dirty during specific time periods can be identified and focus on cleaning during those time periods. Furthermore, it can also focus on cleaning areas that are particularly prone to getting dirty after a specific event. For example, areas that are particularly prone to getting dirty can be focused on cleaning after an event is held in an apartment building. This allows the cleaning unit to clean common areas efficiently and effectively.

[0037] During cleaning, the cleaning unit can monitor the usage status of the common areas in real time and determine the optimal cleaning timing. Methods for monitoring the usage status of the common areas include, for example, sensors and cameras. A sensor is a device that detects the usage status of the common areas and determines the cleaning timing based on the data. For example, a sensor can be used to identify time periods when the common areas are less used and clean during those time periods. A camera is a device that monitors video of the common areas in real time and determines the cleaning timing based on the data. For example, a camera can be used to perform cleaning by avoiding time periods when the common areas are more frequently used. This allows the cleaning timing to be adjusted according to the usage status of the common areas. Furthermore, the cleaning unit determines the optimal cleaning timing based on the usage status of the common areas. For example, cleaning can be performed during time periods when the common areas are less used. It can also perform cleaning during specific time periods. For example, it can identify time periods when the common areas are less used and clean during those time periods. It can also perform cleaning by avoiding time periods when the common areas are more frequently used. For example, it can avoid time periods when the common areas are more frequently used and clean during times other than those time periods. This allows the cleaning unit to determine the optimal cleaning timing according to the usage status of the common areas and perform efficient cleaning.

[0038] The monitoring unit can analyze past equipment abnormality history and focus on monitoring equipment that is particularly prone to abnormalities. Methods for analyzing past equipment abnormality history include, for example, using an abnormality log or a database. An abnormality log is data that records abnormality occurrences in equipment and is a system for managing past abnormality history. For example, the abnormality log can be used to identify equipment that is particularly prone to abnormalities and focus on monitoring it. A database is a system that accumulates abnormality occurrences in equipment and manages past abnormality history. For example, the database can be used to identify equipment that is particularly prone to abnormalities during a specific time period and focus on monitoring it. This enables efficient monitoring by analyzing past equipment abnormality history. Furthermore, the monitoring unit can focus on monitoring equipment that is particularly prone to abnormalities. For example, the monitoring unit can identify equipment that is particularly prone to abnormalities from past equipment abnormality history and focus on monitoring it. It can also focus on monitoring equipment that is particularly prone to abnormalities during a specific time period. For example, it can identify equipment that is particularly prone to abnormalities during a specific time period and focus on monitoring it during that time period. It can also focus on monitoring equipment that is particularly prone to abnormalities after a specific event. For example, after an event is held in an apartment building, the monitoring unit can focus on monitoring equipment that is particularly prone to malfunctions, thereby enabling the monitoring unit to monitor the equipment efficiently and effectively.

[0039] When monitoring equipment, the monitoring unit can monitor the equipment usage status in real time and determine the optimal monitoring timing. Methods for monitoring the equipment usage status include, for example, sensors and cameras. A sensor is a device that detects the equipment usage status and determines the monitoring timing based on the data. For example, a sensor can be used to identify time periods when the equipment is less used and monitor during those time periods. A camera is a device that monitors video of the equipment in real time and determines the monitoring timing based on the data. For example, a camera can be used to perform monitoring while avoiding time periods when the equipment is heavily used. This makes it possible to adjust the monitoring timing according to the equipment usage status. Furthermore, the monitoring unit determines the optimal monitoring timing based on the equipment usage status. For example, monitoring can be performed during time periods when the equipment is less used. Monitoring can also be performed during specific time periods. For example, a time period when the equipment is less used can be identified and monitoring can be performed during that time period. Furthermore, monitoring can be performed while avoiding time periods when the equipment is heavily used. For example, monitoring can be performed outside of that time period to avoid time periods when the equipment is heavily used. This allows the monitoring unit to determine the optimal monitoring timing according to the equipment usage status and perform efficient monitoring.

[0040] The reporting department can analyze past report history and prioritize reporting of particularly important information. Methods for analyzing past report history include, for example, a report database and log analysis. A report database is a system for storing and managing past reports. For example, a report database can be used to extract particularly important information and prioritize reporting. Log analysis is a technique for analyzing report creation history and identifying particularly important information. For example, log analysis can be used to report important information during a specific time period. By analyzing past report history, important information can be reported quickly. Furthermore, the reporting department prioritizes reporting of particularly important information. For example, particularly important information can be extracted from past report history and prioritized for reporting. Important information can also be reported during a specific time period. For example, important information can be identified during a specific time period and prioritized for reporting during that time period. Furthermore, important information can be reported after a specific event. For example, especially important information can be prioritized for reporting after an event is held in an apartment building. This allows the reporting department to report important information efficiently and effectively.

[0041] When creating a report, the reporting unit can evaluate the reliability of the data collected from the sensors in real time and determine the optimal report content. Methods for evaluating the reliability of the data collected from the sensors include, for example, data consistency checks and outlier detection. Data consistency checks are a method for checking whether the data collected from the sensors is consistent. For example, data consistency checks can be used to check whether the data collected from the sensors is accurate. Outlier detection is a method for detecting whether the data collected from the sensors contains an abnormal value. For example, outlier detection can be used to issue an alert when the data collected from the sensors contains an abnormal value. This allows the reliability of the data collected from the sensors to be evaluated. Furthermore, the reporting unit determines the optimal report content based on the reliability of the data collected from the sensors. For example, a report can be created based on highly reliable data. It is also possible to focus reporting on specific data. For example, reporting can be focused on specific time periods based on highly reliable data. Furthermore, the reliability of the data collected from the sensors can be evaluated in real time and the optimal report content can be determined. For example, the reliability of the data collected from the sensors can be evaluated in real time and the report content can be determined based on the evaluation results. This allows the reporting unit to provide accurate and reliable reports.

[0042] When creating a report, the reporting unit can optimize the report content by taking into account the geographical conditions of the report target. Methods for taking into account geographical conditions include, for example, regional characteristics and climatic conditions. Regional characteristics are a method for taking into account the characteristics of the region of the report target. For example, report content suitable for a specific region can be selected based on regional characteristics. Climatic conditions are a method for taking into account the climate of the region of the report target. For example, report content suitable for a specific climate can be selected based on climatic conditions. This allows the report content to be optimized by taking into account geographical conditions. Furthermore, the reporting unit optimizes the report content based on geographical conditions. For example, the reporting unit selects optimal report content by taking into account regional characteristics of the report target. Furthermore, the reporting unit can select optimal report content by taking into account climatic conditions of the report target. For example, the reporting unit adjusts the report content based on specific regional characteristics and climatic conditions. Furthermore, the reporting unit can adjust the report schedule by taking into account the geographical conditions of the report target. For example, the reporting schedule is adjusted based on specific regional characteristics and climatic conditions. This allows the reporting unit to provide optimal report content according to the geographical conditions of the report target.

[0043] When creating a report, the reporting department can analyze the social media activity of the report target and provide related report information. Methods for analyzing social media activity include, for example, post content and engagement. Post content is a method for analyzing the social media posts of the report target. For example, the need for a report can be analyzed based on the post content. Engagement is a method for analyzing the engagement (likes, comments, shares, etc.) of the report target on social media. For example, the report content can be adjusted based on the engagement. In this way, the social media activity can be analyzed and related report information can be provided. Furthermore, the reporting department can optimize the report content based on the social media activity. For example, the need for a report can be analyzed from the social media activity of the report target. In addition, the report content can be adjusted based on the social media activity of the report target. For example, the report content can be adjusted based on specific post content and engagement. Furthermore, the social media activity of the report target can be analyzed and the reporting method can be optimized. For example, the optimal reporting method can be selected based on the social media activity. In this way, the reporting department can analyze the social media activity of the report target and provide related report information.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The reception unit can monitor the health status of visitors and take appropriate action if an abnormality is detected. For example, when a visitor arrives at the entrance, a non-contact thermometer is used to measure the visitor's temperature, and if a fever is detected, the resident of the visitor's location is notified. Also, if a visitor complains of feeling unwell, the reception unit can automatically notify emergency contacts to prompt a prompt response. Furthermore, the reception unit can also provide priority elevator access based on the visitor's health status. This allows the reception unit to monitor the visitor's health status and take appropriate action to ensure the safety of residents and visitors.

[0046] The cleaning unit is equipped with AI, allowing it to automatically determine the type and level of dirt in the cleaning area and select the optimal cleaning method. For example, the cleaning robot uses a camera to detect dirt on the floor, and if the dirt is oily, it will use a special cleaning solution to clean it. Also, if the cleaning robot detects dirt on the carpet, it can adjust the suction power to clean it more effectively. Furthermore, the cleaning robot can monitor the congestion status of the cleaning area in real time and clean during less crowded times. This allows the cleaning unit to perform the optimal cleaning according to the status of the cleaning area.

[0047] The monitoring unit constantly monitors the air quality within the apartment building and can notify the manager if an abnormality is detected. For example, the monitoring unit uses air quality sensors to monitor the carbon dioxide concentration and volatile organic compound (VOC) concentration within the apartment building and issues an alert if the standard values ​​are exceeded. The monitoring unit can also automatically control the ventilation system based on the air quality data to improve air quality. Furthermore, the monitoring unit can regularly send air quality data as a report to the management association and propose measures to protect the health of residents. This allows the monitoring unit to monitor the air quality within the apartment building and take appropriate measures to protect the health of residents.

[0048] The reporting department can analyze energy consumption data within the apartment building and make suggestions for improving energy efficiency. For example, the reporting department can collect power consumption data for each dwelling unit and analyze energy consumption trends. The reporting department can also analyze energy consumption data for lighting and air conditioning equipment in common areas and suggest efficient operation methods. Furthermore, the reporting department can provide energy-saving advice to residents based on the energy consumption data and encourage them to reduce their energy consumption. In this way, the reporting department can improve the energy efficiency of the entire apartment building and contribute to reducing the environmental impact.

[0049] The monitoring unit constantly monitors noise levels within the apartment building and can notify the manager if an abnormality is detected. For example, the monitoring unit can use noise sensors to monitor noise levels within the apartment building and issue an alert if the level exceeds a standard value. The monitoring unit can also identify the source of the noise and propose appropriate countermeasures. Furthermore, the monitoring unit can provide residents with advice on noise control measures based on noise level data, thereby improving the living environment. This allows the monitoring unit to monitor noise levels within the apartment building and take appropriate measures to maintain a comfortable living environment for residents.

[0050] The reporting department can analyze crime prevention data within the apartment building and make proposals to strengthen crime prevention measures. For example, the reporting department can analyze video data from security cameras in the entrance and common areas to detect suspicious activity. The reporting department can also collect crime prevention reports from residents and store them in a database. Furthermore, the reporting department can make proposals to strengthen crime prevention measures within the apartment building based on the crime prevention data, thereby ensuring the safety of residents. In this way, the reporting department can strengthen crime prevention measures within the apartment building and improve the sense of security of residents.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The reception unit authenticates and notifies the visitor. For example, facial recognition technology can be used to authenticate the visitor and notify the resident of the location. Visitors can also be authenticated using a 2D code or ID card. Facial recognition technology uses deep learning to recognize the visitor's face and perform authentication. When using a 2D code or ID card, authentication is performed by the visitor scanning the 2D code or presenting their ID card. Step 2: The cleaning unit cleans the common areas. For example, a robot vacuum cleaner is used to clean the common areas. The robot vacuum cleaner operates according to a pre-set schedule and cleans the common areas. The robot vacuum cleaner has suction power, battery life, and sensor functions to clean efficiently. Step 3: The monitoring unit monitors the status of the equipment. For example, sensors are used to constantly monitor the status of the equipment, and notify the administrator if an abnormality occurs. A temperature sensor monitors the temperature of the equipment and notifies the administrator if there is an abnormal temperature change. A humidity sensor monitors the humidity of the equipment and notifies the administrator if there is an abnormal humidity change. An operation sensor monitors the operating status of the equipment and notifies the administrator if an abnormality occurs. Step 4: The reporting department analyzes the data and creates a report. For example, it uses generation AI to analyze the data collected from the sensors, creates periodic reports, and sends them to the management association. The generation AI analyzes the data using natural language generation and data analysis algorithms and creates a report. The generation AI performs statistical analysis based on the data collected from the sensors and creates a report. It uses data analysis algorithms to analyze the data and create a report.

[0053] (Example 2) A condominium management system according to an embodiment of the present invention works with local governments to address the aging population problem in condominiums by utilizing robots and IoT devices to revolutionize condominium management. This condominium management system automates reception, cleaning, inspections, patrols, and reporting tasks, reducing the burden on management associations in condominiums with a large elderly population. Specifically, reception tasks are automated by a robot that receives visitors at the entrance, authenticates them using facial recognition technology, and notifies the residents of the visit. Cleaning tasks are automated by a robot vacuum cleaner that cleans common areas and operates according to a pre-set schedule. Inspection and patrol tasks are automated by an IoT device that constantly monitors the status of various facilities within the condominium and notifies the manager via a smartphone app if an abnormality occurs. Reporting tasks are automated by a generative AI that analyzes data collected from sensors, regularly creates reports, and sends them to the condominium association by email. This system reduces the burden on management associations in condominiums with a large elderly population and creates an environment where seniors can live with peace of mind. It also prevents depopulation and enables the establishment of a sustainable condominium management system. This will reduce the burden on management associations in apartment complexes with a large elderly population, and create an environment where the elderly can live with peace of mind. It will also help prevent marginalized communities and enable the creation of a sustainable management system for apartment complexes.

[0054] An apartment management system according to an embodiment includes a reception unit, a cleaning unit, a monitoring unit, and a reporting unit. The reception unit authenticates and notifies visitors. For example, the reception unit authenticates visitors using facial recognition technology and notifies the residents of the residence. The reception unit can also authenticate visitors using a two-dimensional code (e.g., a QR code) or an ID card. For example, facial recognition technology uses deep learning to recognize the visitor's face and perform authentication. When a two-dimensional code or an ID card is used, authentication is performed by the visitor scanning the two-dimensional code or presenting the ID card. The cleaning unit cleans common areas. For example, the cleaning unit cleans common areas using a robot vacuum cleaner. The robot vacuum cleaner operates according to a pre-set schedule to clean the common areas. For example, the robot vacuum cleaner has suction power, battery life, and sensor functions, allowing for efficient cleaning. The monitoring unit monitors the status of equipment. For example, the monitoring unit constantly monitors the status of equipment using sensors and notifies the manager if an abnormality occurs. The sensors may include temperature sensors, humidity sensors, and motion sensors. For example, a temperature sensor monitors the temperature of equipment and notifies the management association if an abnormal temperature change occurs. A humidity sensor monitors the humidity of equipment and notifies the management association if an abnormal humidity change occurs. A motion sensor monitors the operating status of equipment and notifies the management association if an abnormality occurs. The reporting unit analyzes the data and creates a report. The reporting unit analyzes the data collected from the sensors using, for example, a generation AI, periodically creates reports, and sends them to the management association. The generation AI analyzes the data using natural language generation and data analysis algorithms and creates reports. For example, the generation AI performs statistical analysis based on the data collected from the sensors and creates reports. The generation AI also analyzes the data using a data analysis algorithm and creates reports. As a result, the condominium management system according to the embodiment automates reception, cleaning, monitoring, and reporting tasks, thereby reducing the burden on management associations in condominiums with a large elderly population.

[0055] The reception unit can authenticate visitors using facial recognition technology and notify the residents of the location. Examples of facial recognition technology include deep learning and pattern matching. Deep learning is a technology that learns from large amounts of facial image data to recognize visitors' faces with high accuracy. For example, facial recognition technology using deep learning captures the visitor's face with a camera and performs authentication using a trained model. Pattern matching compares the visitor's facial image with a pre-registered facial image and performs authentication based on the degree of match. For example, facial recognition technology using pattern matching compares the visitor's facial image with facial images in a database and authenticates the visitor if the degree of match is high. This improves the accuracy of visitor authentication using facial recognition technology. Furthermore, the reception unit notifies the residents of the location of the visitor of information about the authenticated visitor. Notification methods include, for example, email, SMS, and app notification. For example, email notification is a method of sending visitor information to the resident's email address. SMS notification is a method of sending visitor information to the resident's mobile phone via SMS. App notification is a method of notifying residents of visitor information through a dedicated app, which allows for quick and accurate visitor authentication and notification.

[0056] The cleaning unit can clean common areas using a robot vacuum cleaner. The robot vacuum cleaner's characteristics include, for example, suction power, battery life, and sensor functions. Suction power is the ability of the robot vacuum cleaner to suck up dirt and dust and affects cleaning efficiency. For example, a robot vacuum cleaner with strong suction power can effectively suck up fine dirt and dust. Battery life indicates how long the robot vacuum cleaner can operate on a single charge. For example, a robot vacuum cleaner with a long battery life can clean a large area at once. Sensor functions enable the robot vacuum cleaner to detect and avoid obstacles. For example, a robot vacuum cleaner with sensor functions can clean efficiently without bumping into furniture or walls. This improves the efficiency of cleaning work by using a robot vacuum cleaner. Furthermore, the cleaning unit can operate the robot vacuum cleaner according to a pre-set schedule. For example, the cleaning unit can operate the robot vacuum cleaner at a set time every day to clean common areas. The cleaning unit can also operate the robot vacuum cleaner to clean after a specific event. For example, the cleaning department operates the robot vacuum cleaner to clean the common areas after an event is held in the apartment building, which allows the cleaning department to clean the common areas efficiently and effectively.

[0057] The monitoring unit constantly monitors the status of the equipment using sensors and can notify an administrator if an abnormality occurs. Examples of sensors include temperature sensors, humidity sensors, and operation sensors. The temperature sensor monitors the temperature of the equipment and notifies an administrator if an abnormal temperature change occurs. For example, the temperature sensor issues an alert if the temperature of the equipment exceeds a set range. The humidity sensor monitors the humidity of the equipment and notifies an administrator if an abnormal humidity change occurs. For example, the humidity sensor issues an alert if the humidity of the equipment exceeds a set range. The operation sensor monitors the operating status of the equipment and notifies an administrator if an abnormality occurs. For example, the operation sensor issues an alert if the equipment is not operating normally. This enables the use of sensors to quickly detect and respond to abnormalities in the equipment. Furthermore, the monitoring unit notifies an administrator if an abnormality occurs. Notification methods include, for example, email, SMS, and app notification. For example, email notification is a method of sending details of the abnormality to the administrator's email address. SMS notification is a method of sending details of the abnormality to the administrator's mobile phone via SMS. App notification is a method of notifying an administrator of details of the abnormality through a dedicated app. This allows the monitoring unit to quickly detect abnormalities in the equipment and notify the administrator.

[0058] The reporting department can use generative AI to analyze data collected from sensors, periodically create reports, and send them to the management association. Generative AI includes, for example, natural language generation and data analysis algorithms. Natural language generation is a technology that generates natural-sounding sentences based on data. For example, generative AI automatically creates reports based on data collected from sensors. Data analysis algorithms are a technology that analyzes data and extracts meaningful information. For example, generative AI analyzes data collected from sensors and detects outliers and trends. This automates the creation and transmission of reports, reducing the burden on the management association. Furthermore, the reporting department periodically creates reports and sends them to the management association. The contents of the reports include, for example, the status of equipment, occurrence of abnormalities, and cleaning implementation status. For example, the reporting department creates a report summarizing the status of equipment each month and sends it to the management association. In addition, the reporting department can also include details of any abnormalities in the report and send it to the management association. This allows the reporting department to quickly and accurately report the status of equipment and occurrence of abnormalities to the management association.

[0059] The reception unit can estimate the visitor's emotions and adjust the authentication process based on the estimated emotions. Methods for estimating the visitor's emotions include, for example, facial expression recognition and voice analysis. Facial expression recognition is a technology that analyzes the visitor's facial expressions to estimate their emotions. For example, facial expression recognition can be used to determine whether the visitor is nervous or relaxed. Voice analysis is a technology that analyzes the tone and speed of the visitor's voice to estimate their emotions. For example, voice analysis can be used to determine whether the visitor is in a hurry or calm. This allows for accurate estimation of the visitor's emotions. Furthermore, the reception unit adjusts the authentication process based on the estimated emotions of the visitor. For example, if the visitor is nervous, the authentication process can be simplified and performed quickly. On the other hand, if the visitor is relaxed, a detailed authentication process can be performed to enhance security. If the visitor is in a hurry, voice authentication can be prioritized and authentication can be completed quickly. This improves the visitor's experience by adjusting the authentication process according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may input facial expression data of a visitor into the generation AI and have the generation AI estimate the emotion.

[0060] The reception unit can analyze past visit history and simplify the authentication process for frequent visitors. Methods for analyzing past visit history include, for example, databases and log analysis. A database is a system for storing visitor information and managing past visit history. For example, a database can be used to extract information about frequent visitors and simplify the authentication process. Log analysis is a technology for analyzing visitor access logs and identifying frequent visitors. For example, log analysis can be used to identify visitors who frequently visit during a specific time period and simplify the authentication process. This analysis of past visit history speeds up authentication of frequent visitors. Furthermore, the reception unit simplifies the authentication process for frequent visitors. For example, it can automatically recognize visitors who have visited frequently in the past and provide a simplified authentication process. It can also simplify the authentication process for visitors who visit during a specific time period. For example, it can register authentication information in advance for visitors who frequently visit during a specific time period and perform rapid authentication. This allows the reception unit to optimize the authentication process based on past visit history and improve convenience for visitors.

[0061] When authenticating a visitor, the reception unit can adjust the timing of notifications taking into account the schedule of the resident of the destination. Methods for acquiring the schedule of the resident of the destination include, for example, a calendar app or manual input. A calendar app is an application for managing the resident's schedule and can automatically acquire the resident's schedule. For example, the calendar app can be used to acquire the resident's meeting or outing plans and adjust the timing of notifications. Manual input is a method in which the resident manually inputs their own schedule. For example, the resident manually inputs their own schedule, and the reception unit can adjust the timing of notifications based on that information. This allows the timing of notifications to be adjusted according to the schedule of the resident of the destination. Furthermore, the reception unit adjusts the timing of notifications based on the resident's schedule. For example, if the resident is in a meeting, the reception unit can delay notifications. Also, if the resident is out, the reception unit can cancel notifications. Furthermore, if the resident is on a break, the reception unit can reduce notifications. This allows the timing of notifications to be adjusted according to the resident's schedule, improving convenience for the resident.

[0062] The cleaning unit can estimate the emotions of residents in the cleaning area and adjust the cleaning schedule based on the estimated emotions. Methods for estimating resident emotions include, for example, questionnaires and sensor analysis. A questionnaire is a method of asking residents questions about their emotions and estimating their emotions based on their responses. For example, a questionnaire can be used to determine whether a resident is relaxed or stressed. Sensor analysis is a method of analyzing a resident's biometric data to estimate their emotions. For example, a sensor can be used to measure a resident's heart rate and electrodermal activity and estimate their emotions based on that data. This allows for accurate estimation of the resident's emotions. Furthermore, the cleaning unit adjusts the cleaning schedule based on the estimated resident's emotions. For example, if a resident is relaxed, the cleaning schedule can be carried out as usual. If a resident is stressed, the cleaning schedule can be changed to reduce the resident's stress. Furthermore, if a resident is in a hurry, the cleaning schedule can be adjusted to speed up cleaning. This improves the resident's comfort by adjusting the cleaning schedule according to the resident's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the cleaning unit may be performed using AI, or may be performed without using AI. For example, the cleaning unit may input biometric data of a resident into the generation AI and have the generation AI estimate emotions.

[0063] The cleaning unit can analyze past cleaning history and focus on cleaning areas that are particularly prone to getting dirty. Methods for analyzing past cleaning history include, for example, cleaning logs and databases. A cleaning log is data that records the status of cleaning implementation and is a system for managing past cleaning history. For example, a cleaning log can be used to identify areas that are particularly prone to getting dirty and focus on cleaning them. A database is a system for accumulating cleaning implementation status and managing past cleaning history. For example, a database can be used to identify areas that are particularly prone to getting dirty during specific time periods and focus on cleaning them. This enables efficient cleaning by analyzing past cleaning history. Furthermore, the cleaning unit can focus on cleaning areas that are particularly prone to getting dirty. For example, an area that is particularly prone to getting dirty can be identified from past cleaning history and focus on cleaning it. It can also focus on cleaning areas that are particularly prone to getting dirty during specific time periods. For example, an area that is particularly prone to getting dirty during specific time periods can be identified and focus on cleaning during those time periods. Furthermore, it can also focus on cleaning areas that are particularly prone to getting dirty after a specific event. For example, areas that are particularly prone to getting dirty can be focused on cleaning after an event is held in an apartment building. This allows the cleaning unit to clean common areas efficiently and effectively.

[0064] During cleaning, the cleaning unit can monitor the usage status of the common areas in real time and determine the optimal cleaning timing. Methods for monitoring the usage status of the common areas include, for example, sensors and cameras. A sensor is a device that detects the usage status of the common areas and determines the cleaning timing based on the data. For example, a sensor can be used to identify time periods when the common areas are less used and clean during those time periods. A camera is a device that monitors video of the common areas in real time and determines the cleaning timing based on the data. For example, a camera can be used to perform cleaning by avoiding time periods when the common areas are more frequently used. This allows the cleaning timing to be adjusted according to the usage status of the common areas. Furthermore, the cleaning unit determines the optimal cleaning timing based on the usage status of the common areas. For example, cleaning can be performed during time periods when the common areas are less used. It can also perform cleaning during specific time periods. For example, it can identify time periods when the common areas are less used and clean during those time periods. It can also perform cleaning by avoiding time periods when the common areas are more frequently used. For example, it can avoid time periods when the common areas are more frequently used and clean during times other than those time periods. This allows the cleaning unit to determine the optimal cleaning timing according to the usage status of the common areas and perform efficient cleaning.

[0065] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. Methods for estimating user emotions include, for example, questionnaires and sensor analysis. A questionnaire is a method of asking a user questions about their emotions and estimating their emotions based on their responses. For example, a questionnaire can be used to determine whether the user is relaxed or stressed. Sensor analysis is a method of analyzing a user's biometric data to estimate their emotions. For example, a sensor can be used to measure the user's heart rate and electrodermal activity and estimate their emotions based on that data. This allows for accurate estimation of the user's emotions. Furthermore, the monitoring unit adjusts the monitoring frequency based on the estimated user emotions. For example, if the user is relaxed, the monitoring frequency can be maintained at a normal level. If the user is stressed, the monitoring frequency can be increased to provide a sense of security. Furthermore, if the user is in a hurry, the monitoring frequency can be reduced to provide a prompt response. This improves the user's sense of security by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit may input the user's biometric data into the generation AI and cause the generation AI to estimate emotions.

[0066] The monitoring unit can analyze past equipment abnormality history and focus on monitoring equipment that is particularly prone to abnormalities. Methods for analyzing past equipment abnormality history include, for example, using an abnormality log or a database. An abnormality log is data that records abnormality occurrences in equipment and is a system for managing past abnormality history. For example, the abnormality log can be used to identify equipment that is particularly prone to abnormalities and focus on monitoring it. A database is a system that accumulates abnormality occurrences in equipment and manages past abnormality history. For example, the database can be used to identify equipment that is particularly prone to abnormalities during a specific time period and focus on monitoring it. This enables efficient monitoring by analyzing past equipment abnormality history. Furthermore, the monitoring unit can focus on monitoring equipment that is particularly prone to abnormalities. For example, the monitoring unit can identify equipment that is particularly prone to abnormalities from past equipment abnormality history and focus on monitoring it. It can also focus on monitoring equipment that is particularly prone to abnormalities during a specific time period. For example, it can identify equipment that is particularly prone to abnormalities during a specific time period and focus on monitoring it during that time period. It can also focus on monitoring equipment that is particularly prone to abnormalities after a specific event. For example, after an event is held in an apartment building, the monitoring unit can focus on monitoring equipment that is particularly prone to malfunctions, thereby enabling the monitoring unit to monitor the equipment efficiently and effectively.

[0067] When monitoring equipment, the monitoring unit can monitor the equipment usage status in real time and determine the optimal monitoring timing. Methods for monitoring the equipment usage status include, for example, sensors and cameras. A sensor is a device that detects the equipment usage status and determines the monitoring timing based on the data. For example, a sensor can be used to identify time periods when the equipment is less used and monitor during those time periods. A camera is a device that monitors video of the equipment in real time and determines the monitoring timing based on the data. For example, a camera can be used to perform monitoring while avoiding time periods when the equipment is heavily used. This makes it possible to adjust the monitoring timing according to the equipment usage status. Furthermore, the monitoring unit determines the optimal monitoring timing based on the equipment usage status. For example, monitoring can be performed during time periods when the equipment is less used. Monitoring can also be performed during specific time periods. For example, a time period when the equipment is less used can be identified and monitoring can be performed during that time period. Furthermore, monitoring can be performed while avoiding time periods when the equipment is heavily used. For example, monitoring can be performed outside of that time period to avoid time periods when the equipment is heavily used. This allows the monitoring unit to determine the optimal monitoring timing according to the equipment usage status and perform efficient monitoring.

[0068] The reporting unit can estimate the manager's emotions and adjust the content of the report based on the estimated manager's emotions. Methods for estimating the manager's emotions include, for example, questionnaires and sensor analysis. A questionnaire is a method of asking the manager questions about their emotions and estimating their emotions based on their responses. For example, a questionnaire can be used to determine whether the manager is tense or relaxed. Sensor analysis is a method of analyzing the manager's biometric data to estimate their emotions. For example, a sensor can be used to measure the manager's heart rate and electrodermal activity and estimate their emotions based on that data. This allows for accurate estimation of the manager's emotions. Furthermore, the reporting unit can adjust the content of the report based on the estimated manager's emotions. For example, if the manager is tense, the reporting unit can provide a concise and reassuring report. On the other hand, if the manager is relaxed, the reporting unit can provide a detailed report. Furthermore, if the manager is in a hurry, the reporting unit can provide a report that allows for quick response. By adjusting the content of the report based on the manager's emotions, the manager's understanding can be deepened. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reporting unit may be performed using AI, or may be performed without using AI. For example, the reporting unit may input biometric data of the administrator into the generation AI and have the generation AI estimate emotions.

[0069] The reporting department can analyze past report history and prioritize reporting of particularly important information. Methods for analyzing past report history include, for example, a report database and log analysis. A report database is a system for storing and managing past reports. For example, a report database can be used to extract particularly important information and prioritize reporting. Log analysis is a technique for analyzing report creation history and identifying particularly important information. For example, log analysis can be used to report important information during a specific time period. By analyzing past report history, important information can be reported quickly. Furthermore, the reporting department prioritizes reporting of particularly important information. For example, particularly important information can be extracted from past report history and prioritized for reporting. Important information can also be reported during a specific time period. For example, important information can be identified during a specific time period and prioritized for reporting during that time period. Furthermore, important information can be reported after a specific event. For example, especially important information can be prioritized for reporting after an event is held in an apartment building. This allows the reporting department to report important information efficiently and effectively.

[0070] When creating a report, the reporting unit can evaluate the reliability of the data collected from the sensors in real time and determine the optimal report content. Methods for evaluating the reliability of the data collected from the sensors include, for example, data consistency checks and outlier detection. Data consistency checks are a method for checking whether the data collected from the sensors is consistent. For example, data consistency checks can be used to check whether the data collected from the sensors is accurate. Outlier detection is a method for detecting whether the data collected from the sensors contains an abnormal value. For example, outlier detection can be used to issue an alert when the data collected from the sensors contains an abnormal value. This allows the reliability of the data collected from the sensors to be evaluated. Furthermore, the reporting unit determines the optimal report content based on the reliability of the data collected from the sensors. For example, a report can be created based on highly reliable data. It is also possible to focus reporting on specific data. For example, reporting can be focused on specific time periods based on highly reliable data. Furthermore, the reliability of the data collected from the sensors can be evaluated in real time and the optimal report content can be determined. For example, the reliability of the data collected from the sensors can be evaluated in real time and the report content can be determined based on the evaluation results. This allows the reporting unit to provide accurate and reliable reports.

[0071] The reporting unit can estimate the manager's emotions and customize the report format based on the estimated manager's emotions. Methods for estimating the manager's emotions include, for example, questionnaires and sensor analysis. A questionnaire is a method of asking the manager questions about their emotions and estimating their emotions based on their responses. For example, a questionnaire can be used to determine whether the manager is tense or relaxed. Sensor analysis is a method of analyzing the manager's biometric data to estimate their emotions. For example, a sensor can be used to measure the manager's heart rate and electrodermal activity and estimate their emotions based on that data. This allows for accurate estimation of the manager's emotions. Furthermore, the reporting unit customizes the report format based on the estimated manager's emotions. For example, if the manager is tense, a concise and highly readable report format can be provided. On the other hand, if the manager is relaxed, a report format containing detailed information can be provided. Furthermore, if the manager is in a hurry, a report format that allows for quick response can be provided. This allows for customizing the report format according to the manager's emotions, thereby deepening the manager's understanding. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reporting unit may be performed using AI, or may be performed without using AI. For example, the reporting unit may input biometric data of the administrator into the generation AI and have the generation AI estimate emotions.

[0072] When creating a report, the reporting unit can optimize the report content by taking into account the geographical conditions of the report target. Methods for taking into account geographical conditions include, for example, regional characteristics and climatic conditions. Regional characteristics are a method for taking into account the characteristics of the region of the report target. For example, report content suitable for a specific region can be selected based on regional characteristics. Climatic conditions are a method for taking into account the climate of the region of the report target. For example, report content suitable for a specific climate can be selected based on climatic conditions. This allows the report content to be optimized by taking into account geographical conditions. Furthermore, the reporting unit optimizes the report content based on geographical conditions. For example, the reporting unit selects optimal report content by taking into account regional characteristics of the report target. Furthermore, the reporting unit can select optimal report content by taking into account climatic conditions of the report target. For example, the reporting unit adjusts the report content based on specific regional characteristics and climatic conditions. Furthermore, the reporting unit can adjust the report schedule by taking into account the geographical conditions of the report target. For example, the reporting schedule is adjusted based on specific regional characteristics and climatic conditions. This allows the reporting unit to provide optimal report content according to the geographical conditions of the report target.

[0073] When creating a report, the reporting department can analyze the social media activity of the report target and provide related report information. Methods for analyzing social media activity include, for example, post content and engagement. Post content is a method for analyzing the social media posts of the report target. For example, the need for a report can be analyzed based on the post content. Engagement is a method for analyzing the engagement (likes, comments, shares, etc.) of the report target on social media. For example, the report content can be adjusted based on the engagement. In this way, the social media activity can be analyzed and related report information can be provided. Furthermore, the reporting department can optimize the report content based on the social media activity. For example, the need for a report can be analyzed from the social media activity of the report target. In addition, the report content can be adjusted based on the social media activity of the report target. For example, the report content can be adjusted based on specific post content and engagement. Furthermore, the social media activity of the report target can be analyzed and the reporting method can be optimized. For example, the optimal reporting method can be selected based on the social media activity. In this way, the reporting department can analyze the social media activity of the report target and provide related report information. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, cleaning unit, monitoring unit, and reporting unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit recognizes the face of a visitor using the camera 42 and microphone 38B of the smart device 14 and performs authentication using the control unit 46A. The cleaning unit controls a robot vacuum cleaner using the control unit 46A of the smart device 14 to clean common areas. The monitoring unit monitors the status of the equipment using sensors in the smart device 14 and notifies the user via the specific processing unit 290 of the data processing device 12 if an abnormality occurs. The reporting unit analyzes data collected from the sensors using the specific processing unit 290 of the data processing device 12 and creates a report. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, cleaning unit, monitoring unit, and reporting unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit recognizes the visitor's face using the camera 42 and microphone 238 of the smart glasses 214 and performs authentication using the control unit 46A. The cleaning unit controls the robot vacuum cleaner using the control unit 46A of the smart glasses 214 to clean the common areas. The monitoring unit monitors the status of the equipment using the sensors of the smart glasses 214 and notifies the user via the specific processing unit 290 of the data processing device 12 if an abnormality occurs. The reporting unit analyzes data collected from the sensors using the specific processing unit 290 of the data processing device 12 and creates a report. === Hard Collateral 1-3 === Each of the multiple elements including the reception unit, cleaning unit, monitoring unit, and reporting unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit recognizes the face of a visitor using the camera 42 and microphone 238 of the headset terminal 314 and performs authentication using the control unit 46A. The cleaning unit controls a robot vacuum cleaner using the control unit 46A of the headset terminal 314 to clean the common areas. The monitoring unit monitors the status of the equipment using the sensors of the headset terminal 314 and notifies the user via the specific processing unit 290 of the data processing device 12 if an abnormality occurs. The reporting unit analyzes data collected from the sensors using the specific processing unit 290 of the data processing device 12 and creates a report. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, cleaning unit, monitoring unit, and reporting unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit recognizes the face of a visitor using the camera 42 and microphone 238 of the robot 414 and performs authentication using the control unit 46A. The cleaning unit controls the robot vacuum cleaner using the control unit 46A of the robot 414 to clean the common areas. The monitoring unit monitors the status of the equipment using the sensors of the robot 414 and notifies the user via the specific processing unit 290 of the data processing device 12 if an abnormality occurs. The reporting unit analyzes data collected from the sensors using the specific processing unit 290 of the data processing device 12 and creates a report.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The reception unit can monitor the health status of visitors and take appropriate action if an abnormality is detected. For example, when a visitor arrives at the entrance, a non-contact thermometer is used to measure the visitor's temperature, and if a fever is detected, the resident of the visitor's location is notified. Also, if a visitor complains of feeling unwell, the reception unit can automatically notify emergency contacts to prompt a prompt response. Furthermore, the reception unit can also provide priority elevator access based on the visitor's health status. This allows the reception unit to monitor the visitor's health status and take appropriate action to ensure the safety of residents and visitors.

[0076] The cleaning unit is equipped with AI, allowing it to automatically determine the type and level of dirt in the cleaning area and select the optimal cleaning method. For example, the cleaning robot uses a camera to detect dirt on the floor, and if the dirt is oily, it will use a special cleaning solution to clean it. Also, if the cleaning robot detects dirt on the carpet, it can adjust the suction power to clean it more effectively. Furthermore, the cleaning robot can monitor the congestion status of the cleaning area in real time and clean during less crowded times. This allows the cleaning unit to perform the optimal cleaning according to the status of the cleaning area.

[0077] The monitoring unit constantly monitors the air quality within the apartment building and can notify the manager if an abnormality is detected. For example, the monitoring unit uses air quality sensors to monitor the carbon dioxide concentration and volatile organic compound (VOC) concentration within the apartment building and issues an alert if the standard values ​​are exceeded. The monitoring unit can also automatically control the ventilation system based on the air quality data to improve air quality. Furthermore, the monitoring unit can regularly send air quality data as a report to the management association and propose measures to protect the health of residents. This allows the monitoring unit to monitor the air quality within the apartment building and take appropriate measures to protect the health of residents.

[0078] The reporting department can analyze energy consumption data within the apartment building and make suggestions for improving energy efficiency. For example, the reporting department can collect power consumption data for each dwelling unit and analyze energy consumption trends. The reporting department can also analyze energy consumption data for lighting and air conditioning equipment in common areas and suggest efficient operation methods. Furthermore, the reporting department can provide energy-saving advice to residents based on the energy consumption data and encourage them to reduce their energy consumption. In this way, the reporting department can improve the energy efficiency of the entire apartment building and contribute to reducing the environmental impact.

[0079] The reception unit can estimate the visitor's emotions and adjust its response to the visitor based on the estimated emotions. For example, if the visitor is nervous, the reception robot can guide the visitor in a gentle voice to help them relax. If the visitor is in a hurry, the reception robot can quickly authenticate the visitor and smoothly guide them to their destination. Furthermore, if the visitor is feeling anxious, the reception robot can provide a detailed explanation to provide a sense of security. This allows the reception unit to improve the visitor's experience by responding according to the visitor's emotions.

[0080] The cleaning unit can estimate the resident's emotions and adjust the cleaning robot's operation based on the estimated emotions. For example, if the resident is relaxed, the cleaning robot operates quietly so as not to disturb the resident's relaxation. Also, if the resident is feeling stressed, the cleaning robot can clean quickly to reduce the resident's stress. Furthermore, if the resident is in a hurry, the cleaning robot can clean efficiently and provide cleaning that fits the resident's schedule. In this way, the cleaning unit can improve the resident's comfort by cleaning in accordance with the resident's emotions.

[0081] The monitoring unit constantly monitors noise levels within the apartment building and can notify the manager if an abnormality is detected. For example, the monitoring unit can use noise sensors to monitor noise levels within the apartment building and issue an alert if the level exceeds a standard value. The monitoring unit can also identify the source of the noise and propose appropriate countermeasures. Furthermore, the monitoring unit can provide residents with advice on noise control measures based on noise level data, thereby improving the living environment. This allows the monitoring unit to monitor noise levels within the apartment building and take appropriate measures to maintain a comfortable living environment for residents.

[0082] The reporting department can analyze crime prevention data within the apartment building and make proposals to strengthen crime prevention measures. For example, the reporting department can analyze video data from security cameras in the entrance and common areas to detect suspicious activity. The reporting department can also collect crime prevention reports from residents and store them in a database. Furthermore, the reporting department can make proposals to strengthen crime prevention measures within the apartment building based on the crime prevention data, thereby ensuring the safety of residents. In this way, the reporting department can strengthen crime prevention measures within the apartment building and improve the sense of security of residents.

[0083] The reception unit can estimate the visitor's emotions and adjust the guidance provided to the visitor based on the estimated emotions. For example, if the visitor is nervous, the reception robot can provide guidance in a gentle voice to help them relax. If the visitor is in a hurry, the reception robot can quickly authenticate the visitor and smoothly guide them to their destination. Furthermore, if the visitor is feeling anxious, the reception robot can provide a detailed explanation to provide a sense of security. This allows the reception unit to improve the visitor's experience by providing guidance that suits the visitor's emotions.

[0084] The cleaning unit can estimate the resident's emotions and adjust the cleaning robot's operation based on the estimated emotions. For example, if the resident is relaxed, the cleaning robot operates quietly so as not to disturb the resident's relaxation. Also, if the resident is feeling stressed, the cleaning robot can clean quickly to reduce the resident's stress. Furthermore, if the resident is in a hurry, the cleaning robot can clean efficiently and provide cleaning that fits the resident's schedule. In this way, the cleaning unit can improve the resident's comfort by cleaning in accordance with the resident's emotions.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The reception unit authenticates and notifies the visitor. For example, facial recognition technology can be used to authenticate the visitor and notify the resident of the location. Visitors can also be authenticated using a 2D code or ID card. Facial recognition technology uses deep learning to recognize the visitor's face and perform authentication. When using a 2D code or ID card, authentication is performed by the visitor scanning the 2D code or presenting their ID card. Step 2: The cleaning unit cleans the common areas. For example, a robot vacuum cleaner is used to clean the common areas. The robot vacuum cleaner operates according to a pre-set schedule and cleans the common areas. The robot vacuum cleaner has suction power, battery life, and sensor functions to clean efficiently. Step 3: The monitoring unit monitors the status of the equipment. For example, sensors are used to constantly monitor the status of the equipment, and notify the administrator if an abnormality occurs. A temperature sensor monitors the temperature of the equipment and notifies the administrator if there is an abnormal temperature change. A humidity sensor monitors the humidity of the equipment and notifies the administrator if there is an abnormal humidity change. An operation sensor monitors the operating status of the equipment and notifies the administrator if an abnormality occurs. Step 4: The reporting department analyzes the data and creates a report. For example, it uses generation AI to analyze the data collected from the sensors, creates periodic reports, and sends them to the management association. The generation AI analyzes the data using natural language generation and data analysis algorithms and creates a report. The generation AI performs statistical analysis based on the data collected from the sensors and creates a report. It uses data analysis algorithms to analyze the data and create a report.

[0087] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0089] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0090] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0105] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0141] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0147] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0148] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0149] 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.

[0150] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0151] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0152] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0153] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0154] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0155] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0158] [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that authenticates and notifies visitors; A cleaning department that cleans common areas, a monitoring unit that monitors the state of the equipment; A reporting department that analyzes the data and prepares reports; Equipped with A system characterized by:

2. The reception unit Uses facial recognition technology to authenticate visitors and notify residents of their location The system of claim 1 .

3. The cleaning unit includes: Use a robot vacuum cleaner to clean common areas The system of claim 1 .

4. The monitoring unit Sensors are used to constantly monitor the status of equipment, and if an abnormality occurs, the administrator is notified. The system of claim 1 .

5. The reporting unit Generative AI is used to analyze data collected from sensors, generate periodic reports, and send them to the management association. The system of claim 1 .

6. The reception unit Estimating visitor emotions and adjusting the authentication process based on the estimated visitor emotions The system of claim 1 .

7. The reception unit Analyze past visit history and simplify the authentication process for frequent visitors The system of claim 1 .

8. The reception unit When authenticating visitors, adjust notification timing to take into account the schedule of the resident being visited. The system of claim 1 .

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