system

Abstract

The system according to this embodiment aims to automate the management operations of condominiums and reduce the burden on the management association. [Solution] The system according to the embodiment comprises a reception unit, a cleaning unit, an inspection unit, a reporting unit, and a monitoring unit. The reception unit automates reception work. The cleaning unit automates cleaning work. The inspection unit automates inspection and patrol work. The reporting unit automates reporting work. The monitoring unit monitors the health status of residents.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the management work of condominiums is carried out manually, and the burden on the management association is large.

[0005] The system according to the embodiment aims to automate the management work of condominiums and reduce the burden on the management association.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a cleaning unit, an inspection unit, a reporting unit, and a monitoring unit. The reception unit automates reception work. The cleaning unit automates cleaning work. The inspection unit automates inspection and patrol work. The reporting unit automates reporting work. The monitoring unit monitors the health status of residents. [Effects of the Invention]

[0007] The system according to this embodiment can automate the management operations of an apartment building and reduce the burden on the management association. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The management system according to an embodiment of the present invention is a system that utilizes robots and IoT devices in cooperation with local governments for condominiums with aging populations. This management system automates reception, cleaning, inspection / patrol, and reporting tasks, reducing the burden on the management association. Furthermore, it complements human support with generative AI, creating an environment where elderly people can live with peace of mind. This helps prevent the decline of communities and supports the ease of living for residents, while also establishing a sustainable management system for condominiums. For example, the management system utilizes robots and IoT devices to automate reception tasks. For instance, it installs a robot that recognizes residents' faces and engages in friendly conversation. This robot also simultaneously checks on the well-being of residents. The management system also introduces a cleaning robot specifically designed for condominiums to automate cleaning tasks. This cleaning robot cleans common areas, elevators, and entrance lobbies, maintaining a comfortable environment. Next, the management system utilizes IoT devices to automate inspection / patrol tasks. For example, it installs security cameras and sensors to collect and analyze data. This enables inspections with higher accuracy than visual inspection. Furthermore, the management system utilizes generative AI to automate reporting tasks. The generating AI analyzes data such as inspection results and cleaning status and reports it to the management association. This system not only reduces the burden on the management association but also creates an environment where elderly people can live with peace of mind. For example, the generating AI can monitor the health status of residents and notify local governments and medical institutions if there are any abnormalities. In addition, robots and IoT devices can support the lives of residents, helping to prevent the decline of communities and supporting the ease of living for residents. Furthermore, this system serves as a foundation for building a sustainable management system for condominiums. For example, by reducing the burden on the building manager, the number of applicants will increase, and the management system will stabilize. Economic effects such as suppression of management fees and reduction of utility costs can also be expected. As a result, the asset value of the condominium will be maintained and increased, and a good living environment will be provided for residents. In this way, the management system can support the lives of residents and reduce the burden on the management association.

[0029] The management system according to this embodiment comprises a reception unit, a cleaning unit, an inspection unit, a reporting unit, and a monitoring unit. The reception unit automates reception duties. The reception unit includes, for example, a robot that recognizes the faces of residents and engages in friendly conversation. The reception unit recognizes the faces of residents using, for example, facial recognition technology and engages in conversation using natural language processing technology. The reception unit takes, for example, a picture of a resident's face with a camera and recognizes it using a facial recognition algorithm. The reception unit compares the resident's face with a database and engages in conversation based on the recognition result. The cleaning unit automates cleaning duties. The cleaning unit includes, for example, a cleaning robot specifically designed for apartment buildings. The cleaning unit cleans, for example, common areas, elevators, entrance lobbies, etc. The cleaning unit includes, for example, a cleaning robot that automatically sets a cleaning route and cleans efficiently. The cleaning unit includes, for example, a cleaning robot that detects dirt using sensors and cleans. The inspection unit automates inspection and patrol duties. The inspection unit collects and analyzes data, including, for example, security cameras and sensors. The inspection unit, for example, has security cameras capture images and sensors detect anomalies. The inspection unit, for example, analyzes the collected data and detects anomalies. The inspection unit, for example, notifies the management association if an anomaly is detected. The reporting unit automates reporting tasks. The reporting unit, for example, analyzes data such as inspection results and cleaning status and reports to the management association. The reporting unit, for example, uses generative AI to analyze data and create reports. The reporting unit, for example, uses generative AI to automatically generate reports based on data. The reporting unit, for example, uses generative AI to automatically summarize the contents of the report and provides it to the management association. The monitoring unit monitors the health status of residents. The monitoring unit, for example, measures the health status of residents with sensors and notifies the local government or medical institutions if there are any anomalies. The monitoring unit, for example, uses generative AI to analyze health data and detect anomalies. The monitoring unit, for example, uses generative AI to predict anomalies based on health data and notifies. The monitoring unit, for example, uses a generating AI to monitor residents' health status in real time and detect abnormalities. As a result, the management system according to this embodiment automates tasks such as reception, cleaning, inspection, reporting, and monitoring, reducing the burden on the management association and creating an environment where elderly people can live with peace of mind.

[0030] The reception area will automate reception duties. Specifically, this will include a robot that recognizes residents' faces and engages in friendly conversation. The reception area will use facial recognition technology to recognize residents' faces and natural language processing technology to conduct conversations. For example, when a resident approaches the apartment building's entrance, a camera will capture their face, and a facial recognition algorithm will match it against a database to identify the resident. If recognition is successful, the robot will greet the resident and provide necessary information. The robot can answer residents' questions and provide directions using natural language processing technology. For example, if a resident asks, "Where is my mail?", the robot will reply, "Your mail is in the mailbox on the right side of the entrance." Furthermore, the reception area will accumulate residents' facial recognition data and be able to provide personalized service based on past conversation history. For example, the robot can offer personalized greetings such as, "Welcome back, how was yesterday's meeting?" This will allow the reception area to provide a friendly service to residents and streamline the apartment building's reception operations.

[0031] The cleaning department will automate cleaning operations, specifically including cleaning robots designed for apartment buildings. These robots will clean common areas, elevators, and entrance lobbies. For example, the cleaning robots will automatically move along pre-set cleaning routes for efficient cleaning. They are equipped with sensors to detect dirt on floors, and when dirt is detected, they will focus their cleaning efforts on that area. Furthermore, the robots can automatically collect and dispose of waste. For instance, they can recognize the location of trash cans, collect the waste, and dispose of it in designated locations. The cleaning robots will also undergo regular maintenance to maintain their cleaning performance. For example, they will monitor their battery level and automatically return to charging stations as needed. This allows the cleaning department to perform apartment cleaning tasks efficiently and effectively, maintaining a clean environment.

[0032] The Inspection Department automates inspection and patrol tasks. Specifically, it collects and analyzes data, including from security cameras and sensors. For example, security cameras capture images of common areas, entrances, and parking lots within the apartment building, while sensors detect the opening and closing of doors and windows, as well as unusual vibrations and sounds. This data is transmitted in real time to a central database for analysis. The Inspection Department analyzes the collected data to detect anomalies. For example, it analyzes camera footage to detect intruders, or analyzes sensor data to detect anomalies such as fires or water leaks. If an anomaly is detected, the Inspection Department notifies the management association to encourage a prompt response. For example, if a fire is detected, the Inspection Department sends an emergency notification to the management association and automatically notifies the fire department. The Inspection Department also conducts regular patrol inspections to maintain the safety of the apartment building. For example, robots patrol the building to check for any anomalies. In this way, the Inspection Department can enhance the safety of the apartment building and provide residents with an environment where they can live with peace of mind.

[0033] The reporting department will automate reporting tasks. Specifically, it will analyze data such as inspection results and cleaning status and report them to the management association. For example, it will use a generation AI to analyze data and create reports. Based on the collected data, the generation AI will automatically analyze whether there are any abnormalities and the progress of cleaning, and generate reports. The reports will include detailed information if abnormalities are detected and a list of areas where cleaning has been completed. Furthermore, the generation AI will automatically summarize the contents of the reports and provide them to the management association. For example, it will summarize lengthy reports into short summaries, extract only the important information, and notify the management association. This will allow the management association to quickly grasp the necessary information and take appropriate action. The reporting department will also automate periodic reporting tasks. For example, it will automatically create reports summarizing monthly inspection results and cleaning status and send them to the management association. This will allow the reporting department to streamline reporting tasks and reduce the burden on the management association.

[0034] The monitoring unit monitors the health status of residents. Specifically, it measures residents' health status using sensors and notifies local governments and medical institutions if abnormalities are detected. For example, sensors installed in residents' rooms measure health data such as heart rate, body temperature, and blood pressure in real time. This data is analyzed using a generative AI, and if an abnormality is detected, it is automatically notified to local governments and medical institutions. The generative AI can predict abnormalities based on health data and respond early. For example, if the heart rate is abnormally high, the generative AI predicts the risk of a heart attack and sends an emergency notification. The generative AI also monitors residents' health status in real time and detects abnormalities. For example, if an abnormal rise in body temperature is detected at night, the generative AI issues a warning to the resident and prompts them to take necessary action. In this way, the monitoring unit can always understand the health status of residents and respond quickly when abnormalities occur. Furthermore, the monitoring unit can accumulate health data and perform long-term health management. For example, it can analyze changes in residents' health status based on past health data and propose preventive measures. In this way, the monitoring unit can maintain the health of residents and provide an environment in which the elderly can live with peace of mind.

[0035] The reception area includes a robot that recognizes residents' faces and engages in friendly conversation. For example, the reception area uses a robot that recognizes residents' faces and engages in friendly conversation. For example, the reception area uses facial recognition technology to recognize residents' faces and natural language processing technology to conduct conversations. For example, the reception area photographs residents' faces with a camera and recognizes them using a facial recognition algorithm. For example, the reception area compares residents' faces with a database and conducts conversations based on the recognition results. This allows for smoother communication with residents by recognizing their faces and engaging in friendly conversations.

[0036] The cleaning department includes cleaning robots specifically designed for apartment buildings. For example, the cleaning department uses cleaning robots specifically designed for apartment buildings. The cleaning department cleans common areas, elevators, entrance lobbies, etc. For example, the cleaning robots automatically set cleaning routes and clean efficiently. For example, the cleaning robots use sensors to detect dirt and then clean. This allows for efficient cleaning of common areas, elevators, entrance lobbies, etc., by using cleaning robots specifically designed for apartment buildings.

[0037] The inspection unit includes security cameras and sensors to collect and analyze data. For example, the security cameras capture images, and the sensors detect anomalies. The inspection unit analyzes the collected data to detect anomalies. For example, if an anomaly is detected, the inspection unit notifies the management association. This allows for highly accurate inspections by collecting and analyzing data using security cameras and sensors.

[0038] The reporting department analyzes data such as inspection results and cleaning status and reports it to the management association. The reporting department uses, for example, a generation AI to analyze the data and create a report. The reporting department uses, for example, a generation AI to automatically generate a report based on the data. The reporting department uses, for example, a generation AI to automatically summarize the contents of the report and provide it to the management association. This allows for increased efficiency in management operations by analyzing data such as inspection results and cleaning status and reporting it to the management association.

[0039] The monitoring unit monitors the health status of residents and notifies local governments and medical institutions if any abnormalities are detected. For example, the monitoring unit measures the health status of residents using sensors and notifies local governments and medical institutions if any abnormalities are detected. For example, the monitoring unit analyzes health data using generative AI and detects abnormalities. For example, the monitoring unit uses generative AI to predict abnormalities based on health data and notifies residents. For example, the monitoring unit uses generative AI to monitor the health status of residents in real time and detect abnormalities. In this way, by monitoring the health status of residents and notifying local governments and medical institutions if any abnormalities are detected, an environment can be created in which elderly people can live with peace of mind.

[0040] The reception desk analyzes residents' past visit history and selects the most appropriate response. For example, the reception desk uses robots to provide appropriate guidance based on places residents frequently visited and services they have used in the past. For example, the reception desk predicts services residents will use at specific times based on their past visit history, and robots prepare in advance. For example, the reception desk analyzes residents' past visit history and provides information on specific events and activities. In this way, by analyzing residents' past visit history, the reception desk can select the most appropriate response and provide residents with appropriate guidance.

[0041] The reception desk provides information to residents based on their current situation and interests upon arrival. For example, if a resident is interested in health, the robot will guide them to health-related events and services. If a resident is looking for a new hobby, the robot will suggest information and events related to hobbies. If a resident has a specific problem, the robot will provide appropriate support and services. This ensures that residents receive relevant information based on their current situation and interests.

[0042] The reception desk provides highly relevant information to residents at the time of check-in, taking into account their geographical location. For example, if a resident is in a specific area, the reception desk will provide information and services related to that area. For example, if a resident is approaching a specific facility, the reception desk will provide information about that facility. For example, if a resident is participating in a specific event, the reception desk will provide information related to that event. In this way, by providing highly relevant information that takes into account the resident's geographical location, it is possible to provide residents with appropriate information.

[0043] The reception desk analyzes residents' social media activity during check-in and provides relevant information. For example, a robot provides information based on events and services that residents have shown interest in on social media. The reception desk analyzes the content of residents' social media posts and suggests relevant information and services. The reception desk provides information related to accounts that residents follow on social media. In this way, by analyzing residents' social media activity, it is possible to provide relevant information and guide residents appropriately.

[0044] The cleaning unit senses the degree of soiling in the area to be cleaned and selects the optimal cleaning method. For example, the cleaning unit uses sensors to detect the degree of soiling in a cleaning robot and selects a powerful cleaning mode as needed. For example, the cleaning unit uses cameras to identify the type of soiling in a cleaning robot and selects an appropriate cleaning method. For example, the cleaning unit uses past cleaning data to allow the cleaning robot to focus on cleaning heavily soiled areas. This allows for efficient cleaning by sensing the degree of soiling in the area to be cleaned and selecting the optimal cleaning method.

[0045] The cleaning department sets an efficient cleaning route by referring to past cleaning history during cleaning. For example, the cleaning department's cleaning robots analyze past cleaning history to set the most efficient route. For example, the cleaning department's cleaning robots prioritize cleaning areas that need cleaning based on past cleaning data. For example, the cleaning department's cleaning robots refer to past cleaning history and focus on cleaning areas that have not been cleaned thoroughly. In this way, by referring to past cleaning history, an efficient cleaning route can be set and cleaning work can be made more efficient.

[0046] The cleaning unit adjusts the cleaning frequency based on the usage frequency of the area to be cleaned. For example, the cleaning unit uses sensors to detect the usage frequency of an area and cleans frequently used areas more often. For example, the cleaning unit uses past usage data to reduce the cleaning frequency of areas with low usage frequency. For example, the cleaning unit uses real-time monitoring of area usage by the cleaning robot and adjusts the cleaning frequency accordingly. This allows for efficient cleaning by adjusting the cleaning frequency based on the usage frequency of the area to be cleaned.

[0047] The cleaning unit selects the optimal cleaning method during cleaning, taking into account the geographical location information of the area to be cleaned. For example, the cleaning unit's cleaning robots refer to the geographical location information of the area to select the optimal cleaning method. For example, the cleaning unit's cleaning robots prioritize cleaning areas that require cleaning based on the geographical location information of the area. For example, the cleaning unit's cleaning robots optimize the cleaning route, taking into account the geographical location information of the area. This allows for efficient cleaning by selecting the optimal cleaning method while considering the geographical location information of the area to be cleaned.

[0048] The cleaning department analyzes social media activity in the area to be cleaned during cleaning and selects the appropriate cleaning method. For example, the cleaning department's cleaning robots analyze the area's social media activity and select the optimal cleaning method. For example, the cleaning department's cleaning robots prioritize cleaning areas that need cleaning based on the area's social media activity. For example, the cleaning department's cleaning robots optimize the cleaning route considering the area's social media activity. This allows for efficient cleaning by analyzing the social media activity in the area to be cleaned and selecting the appropriate cleaning method.

[0049] The inspection unit optimizes the inspection algorithm by referring to past inspection history during inspections. For example, the inspection unit's inspection robot analyzes past inspection history and selects the optimal inspection algorithm. For example, the inspection unit's inspection robot prioritizes inspecting areas that require inspection based on past inspection data. For example, the inspection unit's inspection robot refers to past inspection history and focuses on inspecting areas that have not been thoroughly inspected. In this way, by referring to past inspection history, the inspection algorithm can be optimized and inspections can be performed efficiently.

[0050] The inspection unit performs inspections while considering the attribute information of the object being inspected. For example, the inspection unit's inspection robot refers to the attribute information of the object being inspected and selects the optimal inspection method. For example, the inspection unit's inspection robot prioritizes inspecting areas that require inspection based on the attribute information of the object being inspected. For example, the inspection unit's inspection robot considers the attribute information of the object being inspected and optimizes the inspection route. As a result, more appropriate inspections become possible by considering the attribute information of the object being inspected.

[0051] The inspection unit conducts inspections while considering the geographical distribution of the objects being inspected. For example, the inspection unit's inspection robots refer to the geographical distribution of the objects being inspected and select the optimal inspection method. For example, the inspection unit's inspection robots prioritize inspecting areas that require inspection based on the geographical distribution of the objects being inspected. For example, the inspection unit's inspection robots optimize inspection routes while considering the geographical distribution of the objects being inspected. This makes more efficient inspections possible by considering the geographical distribution of the objects being inspected.

[0052] The inspection unit improves inspection accuracy by referring to relevant literature on the subject being inspected during inspection. For example, the inspection unit's inspection robot refers to relevant literature on the subject being inspected and selects the optimal inspection method. For example, the inspection unit's inspection robot prioritizes inspecting areas that require inspection based on relevant literature on the subject being inspected. For example, the inspection unit's inspection robot considers relevant literature on the subject being inspected and optimizes the inspection route. In this way, the accuracy of inspection can be improved by referring to relevant literature on the subject being inspected.

[0053] The reporting department adjusts the level of detail in reports based on the importance of the data. For example, for highly important data, the generating AI creates a detailed report. For less important data, the generating AI creates a concise report. For moderately important data, the generating AI creates a report with an appropriate level of detail. By adjusting the level of detail in reports based on the importance of the data, more appropriate reporting becomes possible.

[0054] The reporting unit applies different reporting algorithms depending on the data category when reporting. For example, for cleaning data, the generating AI applies a reporting algorithm specialized for cleaning. For inspection data, the generating AI applies a reporting algorithm specialized for inspection. For health data, the generating AI applies a reporting algorithm specialized for health. By applying different reporting algorithms depending on the data category, more appropriate reporting becomes possible.

[0055] The reporting department prioritizes reports based on the data submission timing. For example, the reporting department uses the generation AI to prioritize reports for data with approaching submission deadlines. For example, the reporting department uses the generation AI to postpone reports for data with distant submission deadlines. For example, the reporting department uses the generation AI to create reports with an appropriate priority for data with medium submission deadlines. By prioritizing reports based on data submission timing, more efficient reporting becomes possible.

[0056] The reporting unit adjusts the order of reports based on the relevance of the data. For example, the reporting unit prioritizes generating reports for highly relevant data using its generative AI. For example, the reporting unit postpones generating reports for less relevant data using its generative AI. For example, the reporting unit generates reports for moderately relevant data using its generative AI in an appropriate order. By adjusting the order of reports based on the relevance of the data, more appropriate reporting becomes possible.

[0057] The monitoring unit selects the optimal monitoring method by referring to the resident's past health history during monitoring. For example, the monitoring unit's AI generates and proposes the optimal monitoring method based on the resident's past health history. For example, the monitoring unit refers to the resident's past health data, and its AI generates and selects a monitoring method appropriate to the resident's health status. For example, the monitoring unit analyzes the resident's past health history, and its AI generates and adjusts the monitoring method to predict health risks. As a result, by referring to the resident's past health history, the optimal monitoring method can be selected, enabling efficient monitoring.

[0058] The monitoring unit customizes monitoring methods based on the residents' current living conditions during monitoring. For example, the monitoring unit's generating AI proposes the optimal monitoring method based on the residents' current living conditions. For example, the monitoring unit's generating AI adjusts the frequency and method of monitoring according to the residents' living conditions. For example, the monitoring unit monitors the residents' living conditions in real time, and the generating AI takes appropriate action. This allows for more effective monitoring by customizing monitoring methods based on the residents' current living conditions.

[0059] The monitoring unit selects the optimal monitoring method during monitoring, taking into account the geographical location information of residents. For example, the monitoring unit's AI generates an optimal monitoring method based on the residents' geographical location information. For example, the monitoring unit refers to the residents' geographical location information, and the AI ​​generates an adjustment for the monitoring frequency and method. For example, the monitoring unit monitors the residents' geographical location information in real time, and the AI ​​generates an appropriate response. This allows for more efficient monitoring by selecting the optimal monitoring method while considering the residents' geographical location information.

[0060] The monitoring unit analyzes residents' social media activity during monitoring and proposes monitoring methods. For example, the monitoring unit analyzes residents' social media activity, and the generative AI proposes the optimal monitoring method. For example, the monitoring unit adjusts the frequency and method of monitoring based on residents' social media activity, using the generative AI. For example, the monitoring unit monitors residents' social media activity in real time, and the generative AI takes appropriate action. As a result, by analyzing residents' social media activity, the optimal monitoring method can be proposed, enabling efficient monitoring.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The cleaning unit can sense the degree of soiling in the area to be cleaned and select the optimal cleaning method. For example, the cleaning robot can use sensors to sense the degree of soiling and select a powerful cleaning mode as needed. The cleaning robot can also use a camera to identify the type of soiling and select an appropriate cleaning method. Furthermore, the cleaning robot can refer to past cleaning data and focus on cleaning areas that are heavily soiled. This allows for efficient cleaning by sensing the degree of soiling in the area to be cleaned and selecting the optimal cleaning method.

[0063] The reception area can analyze residents' past visit history and select the most appropriate response. For example, a robot can provide appropriate guidance based on places residents frequently visited and services they used in the past. Furthermore, the robot can predict services residents will use at specific times based on their past visit history and prepare accordingly. In addition, by analyzing residents' past visit history, information about specific events and activities can be provided. This allows the reception area to select the most appropriate response and provide residents with appropriate guidance by analyzing their past visit history.

[0064] The cleaning unit can set efficient cleaning routes by referring to past cleaning history during cleaning. For example, a cleaning robot can analyze past cleaning history and set the most efficient route. Furthermore, the cleaning robot can prioritize cleaning areas that need cleaning based on past cleaning data. In addition, the cleaning robot can refer to past cleaning history and focus on cleaning areas that have not been thoroughly cleaned. This allows for the setting of efficient cleaning routes and streamlining cleaning operations by referring to past cleaning history.

[0065] The inspection unit can optimize its inspection algorithm by referring to past inspection history during inspections. For example, an inspection robot can analyze past inspection history and select the optimal inspection algorithm. Furthermore, the inspection robot can prioritize inspecting areas that require inspection based on past inspection data. Additionally, the inspection robot can refer to past inspection history to focus on areas that have not been thoroughly inspected. This allows for the optimization of the inspection algorithm and more efficient inspections by referencing past inspection history.

[0066] The reporting unit can adjust the level of detail in reports based on the importance of the data. For example, the generating AI can create detailed reports for highly important data, concise reports for less important data, and reports with an appropriate level of detail for moderately important data. By adjusting the level of detail based on the importance of the data, more appropriate reports can be generated.

[0067] The monitoring unit can select the optimal monitoring method by referring to the resident's past health history during monitoring. For example, based on the resident's past health history, the generating AI can propose the optimal monitoring method. Furthermore, by referring to the resident's past health data, the generating AI can select a monitoring method appropriate to their health condition. In addition, by analyzing the resident's past health history, the generating AI can predict health risks and adjust the monitoring method accordingly. This allows for the selection of the optimal monitoring method and efficient monitoring by referring to the resident's past health history.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception department will automate reception duties. This may include, for example, a robot that recognizes residents' faces and engages in friendly conversation. It will use facial recognition technology to recognize residents' faces and natural language processing technology to conduct conversations. It will capture residents' faces with a camera and recognize them using a facial recognition algorithm. It will compare the residents' faces with a database and conduct conversations based on the recognition results. Step 2: The cleaning department automates cleaning operations. This includes, for example, cleaning robots specifically designed for apartment buildings. These robots clean common areas, elevators, entrance lobbies, etc. The cleaning robots automatically set cleaning routes and clean efficiently. The cleaning robots use sensors to detect dirt and then clean. Step 3: The inspection department automates inspection and patrol tasks. This includes, for example, security cameras and sensors to collect and analyze data. Security cameras capture images, and sensors detect anomalies. The collected data is analyzed to identify anomalies. If an anomaly is detected, the management association is notified. Step 4: The reporting department automates reporting tasks. For example, it analyzes data such as inspection results and cleaning status and reports it to the management association. It uses a generation AI to analyze the data and create reports. The generation AI automatically generates reports based on the data. The generation AI automatically summarizes the contents of the report and provides it to the management association. Step 5: The monitoring unit monitors the health status of residents. For example, it measures residents' health status using sensors and notifies local governments and medical institutions if there are any abnormalities. It analyzes health data using generative AI and detects abnormalities. The generative AI predicts abnormalities based on health data and sends notifications. The generative AI monitors residents' health status in real time and detects abnormalities.

[0070] (Example of form 2) The management system according to an embodiment of the present invention is a system that utilizes robots and IoT devices in cooperation with local governments for condominiums with aging populations. This management system automates reception, cleaning, inspection / patrol, and reporting tasks, reducing the burden on the management association. Furthermore, it complements human support with generative AI, creating an environment where elderly people can live with peace of mind. This helps prevent the decline of communities and supports the ease of living for residents, while also establishing a sustainable management system for condominiums. For example, the management system utilizes robots and IoT devices to automate reception tasks. For instance, it installs a robot that recognizes residents' faces and engages in friendly conversation. This robot also simultaneously checks on the well-being of residents. The management system also introduces a cleaning robot specifically designed for condominiums to automate cleaning tasks. This cleaning robot cleans common areas, elevators, and entrance lobbies, maintaining a comfortable environment. Next, the management system utilizes IoT devices to automate inspection / patrol tasks. For example, it installs security cameras and sensors to collect and analyze data. This enables inspections with higher accuracy than visual inspection. Furthermore, the management system utilizes generative AI to automate reporting tasks. The generating AI analyzes data such as inspection results and cleaning status and reports it to the management association. This system not only reduces the burden on the management association but also creates an environment where elderly people can live with peace of mind. For example, the generating AI can monitor the health status of residents and notify local governments and medical institutions if there are any abnormalities. In addition, robots and IoT devices can support the lives of residents, helping to prevent the decline of communities and supporting the ease of living for residents. Furthermore, this system serves as a foundation for building a sustainable management system for condominiums. For example, by reducing the burden on the building manager, the number of applicants will increase, and the management system will stabilize. Economic effects such as suppression of management fees and reduction of utility costs can also be expected. As a result, the asset value of the condominium will be maintained and increased, and a good living environment will be provided for residents. In this way, the management system can support the lives of residents and reduce the burden on the management association.

[0071] The management system according to this embodiment comprises a reception unit, a cleaning unit, an inspection unit, a reporting unit, and a monitoring unit. The reception unit automates reception duties. The reception unit includes, for example, a robot that recognizes the faces of residents and engages in friendly conversation. The reception unit recognizes the faces of residents using, for example, facial recognition technology and engages in conversation using natural language processing technology. The reception unit takes, for example, a picture of a resident's face with a camera and recognizes it using a facial recognition algorithm. The reception unit compares the resident's face with a database and engages in conversation based on the recognition result. The cleaning unit automates cleaning duties. The cleaning unit includes, for example, a cleaning robot specifically designed for apartment buildings. The cleaning unit cleans, for example, common areas, elevators, entrance lobbies, etc. The cleaning unit includes, for example, a cleaning robot that automatically sets a cleaning route and cleans efficiently. The cleaning unit includes, for example, a cleaning robot that detects dirt using sensors and cleans. The inspection unit automates inspection and patrol duties. The inspection unit collects and analyzes data, including, for example, security cameras and sensors. The inspection unit, for example, has security cameras capture images and sensors detect anomalies. The inspection unit, for example, analyzes the collected data and detects anomalies. The inspection unit, for example, notifies the management association if an anomaly is detected. The reporting unit automates reporting tasks. The reporting unit, for example, analyzes data such as inspection results and cleaning status and reports to the management association. The reporting unit, for example, uses generative AI to analyze data and create reports. The reporting unit, for example, uses generative AI to automatically generate reports based on data. The reporting unit, for example, uses generative AI to automatically summarize the contents of the report and provides it to the management association. The monitoring unit monitors the health status of residents. The monitoring unit, for example, measures the health status of residents with sensors and notifies the local government or medical institutions if there are any anomalies. The monitoring unit, for example, uses generative AI to analyze health data and detect anomalies. The monitoring unit, for example, uses generative AI to predict anomalies based on health data and notifies. The monitoring unit, for example, uses a generating AI to monitor residents' health status in real time and detect abnormalities. As a result, the management system according to this embodiment automates tasks such as reception, cleaning, inspection, reporting, and monitoring, reducing the burden on the management association and creating an environment where elderly people can live with peace of mind.

[0072] The reception area will automate reception duties. Specifically, this will include a robot that recognizes residents' faces and engages in friendly conversation. The reception area will use facial recognition technology to recognize residents' faces and natural language processing technology to conduct conversations. For example, when a resident approaches the apartment building's entrance, a camera will capture their face, and a facial recognition algorithm will match it against a database to identify the resident. If recognition is successful, the robot will greet the resident and provide necessary information. The robot can answer residents' questions and provide directions using natural language processing technology. For example, if a resident asks, "Where is my mail?", the robot will reply, "Your mail is in the mailbox on the right side of the entrance." Furthermore, the reception area will accumulate residents' facial recognition data and be able to provide personalized service based on past conversation history. For example, the robot can offer personalized greetings such as, "Welcome back, how was yesterday's meeting?" This will allow the reception area to provide a friendly service to residents and streamline the apartment building's reception operations.

[0073] The cleaning department will automate cleaning operations, specifically including cleaning robots designed for apartment buildings. These robots will clean common areas, elevators, and entrance lobbies. For example, the cleaning robots will automatically move along pre-set cleaning routes for efficient cleaning. They are equipped with sensors to detect dirt on floors, and when dirt is detected, they will focus their cleaning efforts on that area. Furthermore, the robots can automatically collect and dispose of waste. For instance, they can recognize the location of trash cans, collect the waste, and dispose of it in designated locations. The cleaning robots will also undergo regular maintenance to maintain their cleaning performance. For example, they will monitor their battery level and automatically return to charging stations as needed. This allows the cleaning department to perform apartment cleaning tasks efficiently and effectively, maintaining a clean environment.

[0074] The Inspection Department automates inspection and patrol tasks. Specifically, it collects and analyzes data, including from security cameras and sensors. For example, security cameras capture images of common areas, entrances, and parking lots within the apartment building, while sensors detect the opening and closing of doors and windows, as well as unusual vibrations and sounds. This data is transmitted in real time to a central database for analysis. The Inspection Department analyzes the collected data to detect anomalies. For example, it analyzes camera footage to detect intruders, or analyzes sensor data to detect anomalies such as fires or water leaks. If an anomaly is detected, the Inspection Department notifies the management association to encourage a prompt response. For example, if a fire is detected, the Inspection Department sends an emergency notification to the management association and automatically notifies the fire department. The Inspection Department also conducts regular patrol inspections to maintain the safety of the apartment building. For example, robots patrol the building to check for any anomalies. In this way, the Inspection Department can enhance the safety of the apartment building and provide residents with an environment where they can live with peace of mind.

[0075] The reporting department will automate reporting tasks. Specifically, it will analyze data such as inspection results and cleaning status and report them to the management association. For example, it will use a generation AI to analyze data and create reports. Based on the collected data, the generation AI will automatically analyze whether there are any abnormalities and the progress of cleaning, and generate reports. The reports will include detailed information if abnormalities are detected and a list of areas where cleaning has been completed. Furthermore, the generation AI will automatically summarize the contents of the reports and provide them to the management association. For example, it will summarize lengthy reports into short summaries, extract only the important information, and notify the management association. This will allow the management association to quickly grasp the necessary information and take appropriate action. The reporting department will also automate periodic reporting tasks. For example, it will automatically create reports summarizing monthly inspection results and cleaning status and send them to the management association. This will allow the reporting department to streamline reporting tasks and reduce the burden on the management association.

[0076] The monitoring unit monitors the health status of residents. Specifically, it measures residents' health status using sensors and notifies local governments and medical institutions if abnormalities are detected. For example, sensors installed in residents' rooms measure health data such as heart rate, body temperature, and blood pressure in real time. This data is analyzed using a generative AI, and if an abnormality is detected, it is automatically notified to local governments and medical institutions. The generative AI can predict abnormalities based on health data and respond early. For example, if the heart rate is abnormally high, the generative AI predicts the risk of a heart attack and sends an emergency notification. The generative AI also monitors residents' health status in real time and detects abnormalities. For example, if an abnormal rise in body temperature is detected at night, the generative AI issues a warning to the resident and prompts them to take necessary action. In this way, the monitoring unit can always understand the health status of residents and respond quickly when abnormalities occur. Furthermore, the monitoring unit can accumulate health data and perform long-term health management. For example, it can analyze changes in residents' health status based on past health data and propose preventive measures. In this way, the monitoring unit can maintain the health of residents and provide an environment in which the elderly can live with peace of mind.

[0077] The reception area includes a robot that recognizes residents' faces and engages in friendly conversation. For example, the reception area uses a robot that recognizes residents' faces and engages in friendly conversation. For example, the reception area uses facial recognition technology to recognize residents' faces and natural language processing technology to conduct conversations. For example, the reception area photographs residents' faces with a camera and recognizes them using a facial recognition algorithm. For example, the reception area compares residents' faces with a database and conducts conversations based on the recognition results. This allows for smoother communication with residents by recognizing their faces and engaging in friendly conversations.

[0078] The cleaning department includes cleaning robots specifically designed for apartment buildings. For example, the cleaning department uses cleaning robots specifically designed for apartment buildings. The cleaning department cleans common areas, elevators, entrance lobbies, etc. For example, the cleaning robots automatically set cleaning routes and clean efficiently. For example, the cleaning robots use sensors to detect dirt and then clean. This allows for efficient cleaning of common areas, elevators, entrance lobbies, etc., by using cleaning robots specifically designed for apartment buildings.

[0079] The inspection unit includes security cameras and sensors to collect and analyze data. For example, the security cameras capture images, and the sensors detect anomalies. The inspection unit analyzes the collected data to detect anomalies. For example, if an anomaly is detected, the inspection unit notifies the management association. This allows for highly accurate inspections by collecting and analyzing data using security cameras and sensors.

[0080] The reporting department analyzes data such as inspection results and cleaning status and reports it to the management association. The reporting department uses, for example, a generation AI to analyze the data and create a report. The reporting department uses, for example, a generation AI to automatically generate a report based on the data. The reporting department uses, for example, a generation AI to automatically summarize the contents of the report and provide it to the management association. This allows for increased efficiency in management operations by analyzing data such as inspection results and cleaning status and reporting it to the management association.

[0081] The monitoring unit monitors the health status of residents and notifies local governments and medical institutions if any abnormalities are detected. For example, the monitoring unit measures the health status of residents using sensors and notifies local governments and medical institutions if any abnormalities are detected. For example, the monitoring unit analyzes health data using generative AI and detects abnormalities. For example, the monitoring unit uses generative AI to predict abnormalities based on health data and notifies residents. For example, the monitoring unit uses generative AI to monitor the health status of residents in real time and detect abnormalities. In this way, by monitoring the health status of residents and notifying local governments and medical institutions if any abnormalities are detected, an environment can be created in which elderly people can live with peace of mind.

[0082] The reception desk estimates the resident's emotions and adjusts the content and tone of conversation based on the estimated emotions. For example, if a resident is stressed, the robot will speak in a calm tone and engage in conversation to help them relax. For example, if a resident is having fun, the robot will speak in a cheerful tone and offer enjoyable topics. For example, if a resident is in a hurry, the robot will provide necessary information quickly and concisely. This allows for more appropriate responses by adjusting the content and tone of conversation according to the resident's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The reception desk analyzes residents' past visit history and selects the most appropriate response. For example, the reception desk uses robots to provide appropriate guidance based on places residents frequently visited and services they have used in the past. For example, the reception desk predicts services residents will use at specific times based on their past visit history, and robots prepare in advance. For example, the reception desk analyzes residents' past visit history and provides information on specific events and activities. In this way, by analyzing residents' past visit history, the reception desk can select the most appropriate response and provide residents with appropriate guidance.

[0084] The reception desk provides information to residents based on their current situation and interests upon arrival. For example, if a resident is interested in health, the robot will guide them to health-related events and services. If a resident is looking for a new hobby, the robot will suggest information and events related to hobbies. If a resident has a specific problem, the robot will provide appropriate support and services. This ensures that residents receive relevant information based on their current situation and interests.

[0085] The reception desk estimates the emotions of residents and determines the priority of reception based on the estimated emotions. For example, if a resident has an urgent problem, the robot will prioritize their response. For example, if a resident is relaxed, the reception desk will prioritize assisting other residents. For example, if a resident is stressed, the reception desk will respond quickly to alleviate their stress. This allows for more appropriate responses by determining the priority of reception according to the emotions of the residents. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The reception desk provides highly relevant information to residents at the time of check-in, taking into account their geographical location. For example, if a resident is in a specific area, the reception desk will provide information and services related to that area. For example, if a resident is approaching a specific facility, the reception desk will provide information about that facility. For example, if a resident is participating in a specific event, the reception desk will provide information related to that event. In this way, by providing highly relevant information that takes into account the resident's geographical location, it is possible to provide residents with appropriate information.

[0087] The reception desk analyzes residents' social media activity during check-in and provides relevant information. For example, a robot provides information based on events and services that residents have shown interest in on social media. The reception desk analyzes the content of residents' social media posts and suggests relevant information and services. The reception desk provides information related to accounts that residents follow on social media. In this way, by analyzing residents' social media activity, it is possible to provide relevant information and guide residents appropriately.

[0088] The cleaning unit senses the degree of soiling in the area to be cleaned and selects the optimal cleaning method. For example, the cleaning unit uses sensors to detect the degree of soiling in a cleaning robot and selects a powerful cleaning mode as needed. For example, the cleaning unit uses cameras to identify the type of soiling in a cleaning robot and selects an appropriate cleaning method. For example, the cleaning unit uses past cleaning data to allow the cleaning robot to focus on cleaning heavily soiled areas. This allows for efficient cleaning by sensing the degree of soiling in the area to be cleaned and selecting the optimal cleaning method.

[0089] The cleaning department sets an efficient cleaning route by referring to past cleaning history during cleaning. For example, the cleaning department's cleaning robots analyze past cleaning history to set the most efficient route. For example, the cleaning department's cleaning robots prioritize cleaning areas that need cleaning based on past cleaning data. For example, the cleaning department's cleaning robots refer to past cleaning history and focus on cleaning areas that have not been cleaned thoroughly. In this way, by referring to past cleaning history, an efficient cleaning route can be set and cleaning work can be made more efficient.

[0090] The cleaning unit adjusts the cleaning frequency based on the usage frequency of the area to be cleaned. For example, the cleaning unit uses sensors to detect the usage frequency of an area and cleans frequently used areas more often. For example, the cleaning unit uses past usage data to reduce the cleaning frequency of areas with low usage frequency. For example, the cleaning unit uses real-time monitoring of area usage by the cleaning robot and adjusts the cleaning frequency accordingly. This allows for efficient cleaning by adjusting the cleaning frequency based on the usage frequency of the area to be cleaned.

[0091] The cleaning unit estimates the emotions of the areas to be cleaned and determines cleaning priorities based on these estimated emotions. For example, the cleaning unit may have a cleaning robot estimate the emotions of an area and prioritize cleaning areas that are stressful. For example, the cleaning unit may have a cleaning robot estimate the emotions of an area and reduce the cleaning frequency of areas that are relaxed. For example, the cleaning unit may have a cleaning robot estimate the emotions of an area and quickly clean areas that are urgent. This allows for more appropriate cleaning by determining cleaning priorities based on the emotions of the areas to be cleaned. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The cleaning unit selects the optimal cleaning method during cleaning, taking into account the geographical location information of the area to be cleaned. For example, the cleaning unit's cleaning robots refer to the geographical location information of the area to select the optimal cleaning method. For example, the cleaning unit's cleaning robots prioritize cleaning areas that require cleaning based on the geographical location information of the area. For example, the cleaning unit's cleaning robots optimize the cleaning route, taking into account the geographical location information of the area. This allows for efficient cleaning by selecting the optimal cleaning method while considering the geographical location information of the area to be cleaned.

[0093] The cleaning department analyzes social media activity in the area to be cleaned during cleaning and selects the appropriate cleaning method. For example, the cleaning department's cleaning robots analyze the area's social media activity and select the optimal cleaning method. For example, the cleaning department's cleaning robots prioritize cleaning areas that need cleaning based on the area's social media activity. For example, the cleaning department's cleaning robots optimize the cleaning route considering the area's social media activity. This allows for efficient cleaning by analyzing the social media activity in the area to be cleaned and selecting the appropriate cleaning method.

[0094] The inspection unit estimates the emotions of the object being inspected and adjusts the inspection criteria based on the estimated emotions. For example, if the inspection robot estimates the emotions of the object being inspected and it is stressed, the inspection unit will relax the inspection criteria. For example, if the inspection robot estimates the emotions of the object being inspected and it is relaxed, the inspection unit will tighten the inspection criteria. For example, if the inspection robot estimates the emotions of the object being inspected and it is in a hurry, the inspection unit will perform the inspection quickly. This allows for more appropriate inspections by adjusting the inspection criteria based on the emotions of the object being inspected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The inspection unit optimizes the inspection algorithm by referring to past inspection history during inspections. For example, the inspection unit's inspection robot analyzes past inspection history and selects the optimal inspection algorithm. For example, the inspection unit's inspection robot prioritizes inspecting areas that require inspection based on past inspection data. For example, the inspection unit's inspection robot refers to past inspection history and focuses on inspecting areas that have not been thoroughly inspected. In this way, by referring to past inspection history, the inspection algorithm can be optimized and inspections can be performed efficiently.

[0096] The inspection unit performs inspections while considering the attribute information of the object being inspected. For example, the inspection unit's inspection robot refers to the attribute information of the object being inspected and selects the optimal inspection method. For example, the inspection unit's inspection robot prioritizes inspecting areas that require inspection based on the attribute information of the object being inspected. For example, the inspection unit's inspection robot considers the attribute information of the object being inspected and optimizes the inspection route. As a result, more appropriate inspections become possible by considering the attribute information of the object being inspected.

[0097] The inspection unit estimates the emotions of the subject being inspected and adjusts the order in which the inspection results are displayed based on the estimated emotions. For example, if the inspection robot estimates the emotions of the subject being inspected and it is stressed, the inspection unit will prioritize displaying important results. For example, if the inspection robot estimates the emotions of the subject being inspected and it is relaxed, the inspection unit will display detailed results. For example, if the inspection robot estimates the emotions of the subject being inspected and it is in a hurry, the inspection unit will display results quickly. By adjusting the order in which the inspection results are displayed based on the emotions of the subject being inspected, it becomes possible to display more appropriate inspection results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The inspection unit conducts inspections while considering the geographical distribution of the objects being inspected. For example, the inspection unit's inspection robots refer to the geographical distribution of the objects being inspected and select the optimal inspection method. For example, the inspection unit's inspection robots prioritize inspecting areas that require inspection based on the geographical distribution of the objects being inspected. For example, the inspection unit's inspection robots optimize inspection routes while considering the geographical distribution of the objects being inspected. This makes more efficient inspections possible by considering the geographical distribution of the objects being inspected.

[0099] The inspection unit improves inspection accuracy by referring to relevant literature on the subject being inspected during inspection. For example, the inspection unit's inspection robot refers to relevant literature on the subject being inspected and selects the optimal inspection method. For example, the inspection unit's inspection robot prioritizes inspecting areas that require inspection based on relevant literature on the subject being inspected. For example, the inspection unit's inspection robot considers relevant literature on the subject being inspected and optimizes the inspection route. In this way, the accuracy of inspection can be improved by referring to relevant literature on the subject being inspected.

[0100] The reporting unit estimates the emotions of the person being reported on and adjusts the way the report is presented based on the estimated emotions. For example, if the person being reported on is stressed, the generating AI will create a concise and to-the-point report. If the person being reported on is relaxed, the generating AI will create a report that includes detailed information. If the person being reported on is in a hurry, the generating AI will create a report in a format that can be quickly understood. This allows for more appropriate reporting by adjusting the way the report is presented based on the emotions of the person being reported on. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generating AI. Generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The reporting department adjusts the level of detail in reports based on the importance of the data. For example, for highly important data, the generating AI creates a detailed report. For less important data, the generating AI creates a concise report. For moderately important data, the generating AI creates a report with an appropriate level of detail. By adjusting the level of detail in reports based on the importance of the data, more appropriate reporting becomes possible.

[0102] The reporting unit applies different reporting algorithms depending on the data category when reporting. For example, for cleaning data, the generating AI applies a reporting algorithm specialized for cleaning. For inspection data, the generating AI applies a reporting algorithm specialized for inspection. For health data, the generating AI applies a reporting algorithm specialized for health. By applying different reporting algorithms depending on the data category, more appropriate reporting becomes possible.

[0103] The reporting unit estimates the emotions of the person being reported on and adjusts the length of the report based on the estimated emotions. For example, if the person being reported on is in a hurry, the generating AI will create a short, to-the-point report. If the person being reported on is relaxed, the generating AI will create a longer report with detailed explanations. If the person being reported on is excited, the generating AI will create a report with visually stimulating effects. This allows for more appropriate reporting by adjusting the length of the report based on the emotions of the person being reported on. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generating AI. Generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The reporting department prioritizes reports based on the data submission timing. For example, the reporting department uses the generation AI to prioritize reports for data with approaching submission deadlines. For example, the reporting department uses the generation AI to postpone reports for data with distant submission deadlines. For example, the reporting department uses the generation AI to create reports with an appropriate priority for data with medium submission deadlines. By prioritizing reports based on data submission timing, more efficient reporting becomes possible.

[0105] The reporting unit adjusts the order of reports based on the relevance of the data. For example, the reporting unit prioritizes generating reports for highly relevant data using its generative AI. For example, the reporting unit postpones generating reports for less relevant data using its generative AI. For example, the reporting unit generates reports for moderately relevant data using its generative AI in an appropriate order. By adjusting the order of reports based on the relevance of the data, more appropriate reporting becomes possible.

[0106] The monitoring unit estimates the residents' emotions and adjusts the monitoring method based on the estimated emotions. For example, if a resident is feeling stressed, the monitoring unit's generative AI will suggest monitoring methods to reduce stress. For example, if a resident is relaxed, the monitoring unit's generative AI will suggest monitoring methods to maintain that relaxation. For example, if a resident is in a hurry, the monitoring unit's generative AI will suggest monitoring methods that allow for a quick response. By adjusting the monitoring method based on the residents' emotions, more appropriate monitoring becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The monitoring unit selects the optimal monitoring method by referring to the resident's past health history during monitoring. For example, the monitoring unit's AI generates and proposes the optimal monitoring method based on the resident's past health history. For example, the monitoring unit refers to the resident's past health data, and its AI generates and selects a monitoring method appropriate to the resident's health status. For example, the monitoring unit analyzes the resident's past health history, and its AI generates and adjusts the monitoring method to predict health risks. As a result, by referring to the resident's past health history, the optimal monitoring method can be selected, enabling efficient monitoring.

[0108] The monitoring unit customizes monitoring methods based on the residents' current living conditions during monitoring. For example, the monitoring unit's generating AI proposes the optimal monitoring method based on the residents' current living conditions. For example, the monitoring unit's generating AI adjusts the frequency and method of monitoring according to the residents' living conditions. For example, the monitoring unit monitors the residents' living conditions in real time, and the generating AI takes appropriate action. This allows for more effective monitoring by customizing monitoring methods based on the residents' current living conditions.

[0109] The monitoring unit estimates the residents' emotions and determines monitoring priorities based on the estimated emotions. For example, if a resident is stressed, the monitoring unit's generative AI will prioritize monitoring. For example, if a resident is relaxed, the monitoring unit's generative AI will reduce the frequency of monitoring. For example, if a resident is in a hurry, the monitoring unit's generative AI will propose a monitoring method that allows for a quick response. This enables more appropriate monitoring by determining monitoring priorities based on the residents' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The monitoring unit selects the optimal monitoring method during monitoring, taking into account the geographical location information of residents. For example, the monitoring unit's AI generates an optimal monitoring method based on the residents' geographical location information. For example, the monitoring unit refers to the residents' geographical location information, and the AI ​​generates an adjustment for the monitoring frequency and method. For example, the monitoring unit monitors the residents' geographical location information in real time, and the AI ​​generates an appropriate response. This allows for more efficient monitoring by selecting the optimal monitoring method while considering the residents' geographical location information.

[0111] The monitoring unit analyzes residents' social media activity during monitoring and proposes monitoring methods. For example, the monitoring unit analyzes residents' social media activity, and the generative AI proposes the optimal monitoring method. For example, the monitoring unit adjusts the frequency and method of monitoring based on residents' social media activity, using the generative AI. For example, the monitoring unit monitors residents' social media activity in real time, and the generative AI takes appropriate action. As a result, by analyzing residents' social media activity, the optimal monitoring method can be proposed, enabling efficient monitoring.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception desk can estimate the resident's emotions and adjust the content and tone of conversation based on the estimated emotions. For example, if a resident is stressed, the robot can speak in a calm tone and engage in conversation to help them relax. If a resident is having fun, the robot can speak in a cheerful tone and offer enjoyable topics. Furthermore, if a resident is in a hurry, the robot can provide necessary information quickly and concisely. This allows for more appropriate responses by adjusting the content and tone of conversation according to the resident's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The cleaning unit can sense the degree of soiling in the area to be cleaned and select the optimal cleaning method. For example, the cleaning robot can use sensors to sense the degree of soiling and select a powerful cleaning mode as needed. The cleaning robot can also use a camera to identify the type of soiling and select an appropriate cleaning method. Furthermore, the cleaning robot can refer to past cleaning data and focus on cleaning areas that are heavily soiled. This allows for efficient cleaning by sensing the degree of soiling in the area to be cleaned and selecting the optimal cleaning method.

[0115] The inspection unit can estimate the emotions of the object being inspected and adjust the inspection criteria based on the estimated emotions. For example, if the inspection robot estimates the emotions of the object being inspected and it is stressed, the inspection criteria can be relaxed. Conversely, if the inspection robot estimates the emotions of the object being inspected and it is relaxed, the inspection criteria can be made stricter. Furthermore, if the inspection robot estimates the emotions of the object being inspected and it is in a hurry, the inspection can be performed quickly. In this way, adjusting the inspection criteria based on the emotions of the object being inspected enables more appropriate inspections. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The reporting unit can estimate the emotions of the person being reported on and adjust the way the report is presented based on those estimated emotions. For example, if the person being reported on is stressed, the generating AI can create a concise and to-the-point report. If the person being reported on is relaxed, the generating AI can create a report that includes detailed information. Furthermore, if the person being reported on is in a hurry, the generating AI can create a report in a format that can be quickly understood. This allows for more appropriate reporting by adjusting the way the report is presented based on the emotions of the person being reported on. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generating AI. Generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The monitoring unit can estimate residents' emotions and adjust the monitoring method based on the estimated emotions. For example, if a resident is feeling stressed, the generative AI can suggest monitoring methods to reduce stress. If a resident is relaxed, the generative AI can suggest monitoring methods to maintain that relaxation. Furthermore, if a resident is in a hurry, the generative AI can suggest monitoring methods that allow for a quick response. By adjusting the monitoring method based on residents' emotions, more appropriate monitoring becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The reception area can analyze residents' past visit history and select the most appropriate response. For example, a robot can provide appropriate guidance based on places residents frequently visited and services they used in the past. Furthermore, the robot can predict services residents will use at specific times based on their past visit history and prepare accordingly. In addition, by analyzing residents' past visit history, information about specific events and activities can be provided. This allows the reception area to select the most appropriate response and provide residents with appropriate guidance by analyzing their past visit history.

[0119] The cleaning unit can set efficient cleaning routes by referring to past cleaning history during cleaning. For example, a cleaning robot can analyze past cleaning history and set the most efficient route. Furthermore, the cleaning robot can prioritize cleaning areas that need cleaning based on past cleaning data. In addition, the cleaning robot can refer to past cleaning history and focus on cleaning areas that have not been thoroughly cleaned. This allows for the setting of efficient cleaning routes and streamlining cleaning operations by referring to past cleaning history.

[0120] The inspection unit can optimize its inspection algorithm by referring to past inspection history during inspections. For example, an inspection robot can analyze past inspection history and select the optimal inspection algorithm. Furthermore, the inspection robot can prioritize inspecting areas that require inspection based on past inspection data. Additionally, the inspection robot can refer to past inspection history to focus on areas that have not been thoroughly inspected. This allows for the optimization of the inspection algorithm and more efficient inspections by referencing past inspection history.

[0121] The reporting unit can adjust the level of detail in reports based on the importance of the data. For example, the generating AI can create detailed reports for highly important data, concise reports for less important data, and reports with an appropriate level of detail for moderately important data. By adjusting the level of detail based on the importance of the data, more appropriate reports can be generated.

[0122] The monitoring unit can select the optimal monitoring method by referring to the resident's past health history during monitoring. For example, based on the resident's past health history, the generating AI can propose the optimal monitoring method. Furthermore, by referring to the resident's past health data, the generating AI can select a monitoring method appropriate to their health condition. In addition, by analyzing the resident's past health history, the generating AI can predict health risks and adjust the monitoring method accordingly. This allows for the selection of the optimal monitoring method and efficient monitoring by referring to the resident's past health history.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception department will automate reception duties. This may include, for example, a robot that recognizes residents' faces and engages in friendly conversation. It will use facial recognition technology to recognize residents' faces and natural language processing technology to conduct conversations. It will capture residents' faces with a camera and recognize them using a facial recognition algorithm. It will compare the residents' faces with a database and conduct conversations based on the recognition results. Step 2: The cleaning department automates cleaning operations. This includes, for example, cleaning robots specifically designed for apartment buildings. These robots clean common areas, elevators, entrance lobbies, etc. The cleaning robots automatically set cleaning routes and clean efficiently. The cleaning robots use sensors to detect dirt and then clean. Step 3: The inspection department automates inspection and patrol tasks. This includes, for example, security cameras and sensors to collect and analyze data. Security cameras capture images, and sensors detect anomalies. The collected data is analyzed to identify anomalies. If an anomaly is detected, the management association is notified. Step 4: The reporting department automates reporting tasks. For example, it analyzes data such as inspection results and cleaning status and reports it to the management association. It uses a generation AI to analyze the data and create reports. The generation AI automatically generates reports based on the data. The generation AI automatically summarizes the contents of the report and provides it to the management association. Step 5: The monitoring unit monitors the health status of residents. For example, it measures residents' health status using sensors and notifies local governments and medical institutions if there are any abnormalities. It analyzes health data using generative AI and detects abnormalities. The generative AI predicts abnormalities based on health data and sends notifications. The generative AI monitors residents' health status in real time and detects abnormalities.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0128] Each of the multiple elements described above, including the reception, cleaning, inspection, reporting, and monitoring units, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit uses the camera 42 and microphone 38B of the smart device 14 to recognize the faces of residents and conducts conversations using the control unit 46A. The cleaning unit uses the control unit 46A of the smart device 14 to control cleaning robots and clean common areas, elevators, entrance lobbies, etc. The inspection unit uses the camera 42 and sensors of the smart device 14 to collect data and analyzes it using the control unit 46A. The reporting unit uses the specific processing unit 290 of the data processing unit 12 to analyze inspection results and cleaning status and reports them to the management association. The monitoring unit uses the sensors of the smart device 14 to measure the health status of residents and analyzes it using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception, cleaning, inspection, reporting, and monitoring units, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit uses the camera 42 and microphone 238 of the smart glasses 214 to recognize the faces of residents and conducts conversations using the control unit 46A. The cleaning unit uses the control unit 46A of the smart glasses 214 to control cleaning robots and clean common areas, elevators, entrance lobbies, etc. The inspection unit uses the camera 42 and sensors of the smart glasses 214 to collect data and analyzes it using the control unit 46A. The reporting unit uses the specific processing unit 290 of the data processing unit 12 to analyze inspection results and cleaning status and reports to the management association. The monitoring unit uses the sensors of the smart glasses 214 to measure the health status of residents and analyzes it using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the reception, cleaning, inspection, reporting, and monitoring units, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit uses the camera 42 and microphone 238 of the headset terminal 314 to recognize the faces of residents and conducts conversations using the control unit 46A. The cleaning unit uses the control unit 46A of the headset terminal 314 to control cleaning robots and clean common areas, elevators, entrance lobbies, etc. The inspection unit uses the camera 42 and sensors of the headset terminal 314 to collect data and analyzes it using the control unit 46A. The reporting unit uses the specific processing unit 290 of the data processing unit 12 to analyze inspection results and cleaning status and reports to the management association. The monitoring unit uses the sensors of the headset terminal 314 to measure the health status of residents and analyzes it using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the reception, cleaning, inspection, reporting, and monitoring units, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit uses the camera 42 and microphone 238 of the robot 414 to recognize the faces of residents and conducts conversations using the control unit 46A. The cleaning unit controls the cleaning robot using the control unit 46A of the robot 414 to clean common areas, elevators, entrance lobbies, etc. The inspection unit collects data using the camera 42 and sensors of the robot 414 and analyzes it using the control unit 46A. The reporting unit analyzes the inspection results and cleaning status using the specific processing unit 290 of the data processing unit 12 and reports them to the management association. The monitoring unit measures the health status of residents using the sensors of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) The reception department will automate the reception process, The cleaning department will automate cleaning operations, The inspection department automates inspection and patrol tasks, The reporting department automates reporting tasks, It includes a monitoring unit that monitors the health status of residents. A system characterized by the following features. (Note 2) The aforementioned reception unit is This includes a robot that recognizes the faces of residents and engages in friendly conversation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned cleaning unit is Includes cleaning robots specifically designed for apartment buildings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The inspection unit is, This includes security cameras and sensors, and collects and analyzes data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting department, Analyze data such as inspection results and cleaning status, and report to the management association. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, The system monitors the health status of residents and notifies local governments and medical institutions if any abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the emotions of the residents and adjusts the content and tone of conversation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the resident's past visit history and select the most appropriate response method. The system described in Appendix 2, characterized by the features described herein. (Note 9) The aforementioned reception unit is Information will be provided at the time of registration based on the resident's current situation and interests. The system described in Appendix 2, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the emotions of residents and determines the priority of reception based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 11) The aforementioned reception unit is When registering, we provide highly relevant information considering the resident's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 12) The aforementioned reception unit is At the time of registration, we analyze the resident's social media activity and provide relevant information. The system described in Appendix 2, characterized by the features described herein. (Note 13) The aforementioned cleaning unit is It senses the degree of dirtiness in the area to be cleaned and selects the optimal cleaning method. The system described in Appendix 3, characterized by the features described herein. (Note 14) The aforementioned cleaning unit is During cleaning, refer to past cleaning history to set an efficient cleaning route. The system described in Appendix 3, characterized by the features described herein. (Note 15) The aforementioned cleaning unit is During cleaning, adjust the cleaning frequency based on the frequency of use of the area to be cleaned. The system described in Appendix 3, characterized by the features described herein. (Note 16) The aforementioned cleaning unit is The system estimates the emotional state of the area to be cleaned and determines cleaning priorities based on the estimated emotional state. The system described in Appendix 3, characterized by the features described herein. (Note 17) The aforementioned cleaning unit is During cleaning, the optimal cleaning method is selected considering the geographical location information of the area to be cleaned. The system described in Appendix 3, characterized by the features described herein. (Note 18) The aforementioned cleaning unit is During cleaning, analyze social media activity in the area being cleaned and select appropriate cleaning methods. The system described in Appendix 3, characterized by the features described herein. (Note 19) The inspection unit is, The emotions of the subject being examined are estimated, and the criteria for the examination are adjusted based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 20) The inspection unit is, During inspections, the inspection algorithm is optimized by referring to past inspection history. The system described in Appendix 4, characterized by the features described herein. (Note 21) The inspection unit is, During inspections, the inspection should be conducted while taking into account the attribute information of the object being inspected. The system described in Appendix 4, characterized by the features described herein. (Note 22) The inspection unit is, The system estimates the emotions of the subject being examined and adjusts the order in which the examination results are displayed based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 23) The inspection unit is, During inspections, the geographical distribution of the items being inspected should be taken into consideration. The system described in Appendix 4, characterized by the features described herein. (Note 24) The inspection unit is, During inspections, refer to relevant literature on the subject being inspected to improve the accuracy of the inspection. The system described in Appendix 4, characterized by the features described herein. (Note 25) The aforementioned reporting department, The system estimates the emotions being reported and adjusts the way the report is expressed based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 26) The aforementioned reporting department, When reporting, adjust the level of detail in the report based on the importance of the data. The system described in Appendix 5, characterized by the features described herein. (Note 27) The aforementioned reporting department, When reporting, different reporting algorithms are applied depending on the data category. The system described in Appendix 5, characterized by the features described herein. (Note 28) The aforementioned reporting department, The system estimates the emotions being reported and adjusts the length of the report based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 29) The aforementioned reporting department, When reporting, prioritize reports based on when the data was submitted. The system described in Appendix 5, characterized by the features described herein. (Note 30) The aforementioned reporting department, When reporting, adjust the order of reporting based on the relevance of the data. The system described in Appendix 5, characterized by the features described herein. (Note 31) The monitoring unit, We estimate residents' sentiments and adjust monitoring methods based on those estimated sentiments. The system described in Appendix 6, characterized by the features described herein. (Note 32) The monitoring unit, During monitoring, the optimal monitoring method is selected by referring to the residents' past health history. The system described in Appendix 6, characterized by the features described herein. (Note 33) The monitoring unit, During monitoring, the monitoring methods are customized based on the residents' current living conditions. The system described in Appendix 6, characterized by the features described herein. (Note 34) The monitoring unit, Estimate residents' sentiments and determine monitoring priorities based on those estimated sentiments. The system described in Appendix 6, characterized by the features described herein. (Note 35) The monitoring unit, During monitoring, the optimal monitoring method will be selected considering the geographical location information of residents. The system described in Appendix 6, characterized by the features described herein. (Note 36) The monitoring unit, During monitoring, we analyze residents' social media activity and propose monitoring methods. The system described in Appendix 6, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception department will automate the reception process, The cleaning department will automate cleaning operations, The inspection department automates inspection and patrol tasks, The reporting department automates reporting tasks, It includes a monitoring unit that monitors the health status of residents. A system characterized by the following features.

2. The aforementioned reception unit is This includes a robot that recognizes the faces of residents and engages in friendly conversation. The system according to feature 1.

3. The aforementioned cleaning unit is Includes cleaning robots specifically designed for apartment buildings. The system according to feature 1.

4. The inspection unit is, This includes security cameras and sensors, and collects and analyzes data. The system according to feature 1.

5. The aforementioned reporting department, Analyze data such as inspection results and cleaning status, and report to the management association. The system according to feature 1.

6. The monitoring unit, The system monitors the health status of residents and notifies local governments and medical institutions if any abnormalities are detected. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the emotions of the residents and adjusts the content and tone of conversation based on those estimated emotions. The system according to feature 2.

8. The aforementioned reception unit is We analyze the resident's past visit history and select the most appropriate response method. The system according to feature 2.

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