system

Abstract

The system according to this embodiment aims to optimize elevator operation based on residents' lifestyle patterns and to efficiently integrate security measures and energy conservation. [Solution] The system according to the embodiment comprises a lifestyle rhythm learning unit, a standby floor adjustment unit, a visitor handling unit, a package handling unit, a security enhancement unit, and an energy-saving operation unit. The lifestyle rhythm learning unit learns the lifestyle patterns of the residents. The standby floor adjustment unit sets the optimal standby floor based on the lifestyle patterns learned by the lifestyle rhythm learning unit. The visitor handling unit performs priority operation according to the visitors. The package handling unit adjusts operation when packages are being delivered. The security enhancement unit performs access control using facial recognition. The energy-saving operation unit optimizes energy efficiency.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to adjust the operation of the elevator according to the living patterns of residents, the response during visits or when carrying luggage is insufficient, and the efficient integration of security measures and energy conservation is an issue.

[0005] The system according to the embodiment aims to optimize the operation of the elevator based on the living patterns of residents and efficiently integrate security measures and energy conservation.

Means for Solving the Problems

[0006] The system according to this embodiment includes a lifestyle rhythm learning unit, a standby floor adjustment unit, a visitor handling unit, a cargo handling unit, a security enhancement unit, and an energy-saving operation unit. The lifestyle rhythm learning unit learns the lifestyle patterns of residents. The standby floor adjustment unit sets the optimal standby floor based on the lifestyle patterns learned by the lifestyle rhythm learning unit. The visitor handling unit provides priority operation tailored to visitors. The cargo handling unit adjusts operations when cargo is being delivered. The security enhancement unit performs access control using facial recognition. The energy-saving operation unit optimizes energy efficiency. [Effects of the Invention]

[0007] The system according to this embodiment can optimize elevator operation based on residents' lifestyle patterns and efficiently integrate security measures and energy saving. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 Smart Elevator Management AI Agent according to an embodiment of the present invention is a system that innovates elevator systems in high-rise condominiums and improves the comfort and safety of residents. This system learns the lifestyle patterns of residents and sets the optimal waiting floor, so that elevators are waiting during times when residents use them most frequently, thus reducing waiting times. For example, during the morning rush hour, the elevator will be waiting on the first floor so that residents can use it immediately. Next, it supports priority operation for visitors and operational adjustments when loading and unloading goods. For example, when a visitor arrives, the elevator will automatically wait on the first floor to welcome the visitor smoothly. Also, when loading and unloading large quantities of goods, the elevator operation will be adjusted to avoid affecting the use of other residents. Furthermore, it provides support functions for the elderly and people with disabilities and settings for preferred shared floors. For example, when an elderly person uses the elevator, the elevator will operate slowly to allow for smooth boarding and alighting. Also, by setting the shared floor, the elevator will automatically wait on the floor preferred by the resident. It performs optimal operation according to the rush hour and return-home times. For example, during the morning rush hour, the elevator will be waiting on the first floor so that residents can use it immediately. Furthermore, during rush hour, elevators will wait on upper floors to ensure residents can return home smoothly. Access control using facial recognition and a nighttime security mode will be implemented. For example, by recognizing residents' faces, elevator use will be restricted to prevent intruders. In addition, security mode will be implemented at night to restrict elevator use and enhance crime prevention measures. Energy efficiency will be optimized, and air conditioning and lighting will be automatically adjusted. For example, air conditioning and lighting will be automatically turned off during times when the elevators are not in use to save energy. Waiting times and congestion status will be notified in real time via a smartphone app. For example, residents can check the current waiting time and congestion status on the app before using the elevator, allowing them to use the elevator efficiently. In this way, the smart elevator management AI agent will improve resident convenience and elevator efficiency, and provide a sustainable living environment by enhancing security and energy saving.Furthermore, it can provide a stress-free lifestyle with smooth boarding and alighting. This allows the smart elevator management AI agent to learn residents' lifestyle patterns and set optimal waiting floors, thereby optimizing elevator operation and improving convenience and efficiency.

[0029] The smart elevator management AI agent according to this embodiment includes a lifestyle rhythm learning unit, a standby floor adjustment unit, a visitor handling unit, a luggage handling unit, a security enhancement unit, and an energy-saving operation unit. The lifestyle rhythm learning unit learns the lifestyle patterns of residents. The lifestyle rhythm learning unit collects data such as residents' wake-up times, return-home times, and frequency of going out, and learns lifestyle patterns based on this data. The lifestyle rhythm learning unit can use AI to analyze residents' lifestyle patterns and provide information for setting the optimal standby floor. The standby floor adjustment unit sets the optimal standby floor based on the lifestyle patterns learned by the lifestyle rhythm learning unit. The standby floor adjustment unit shortens waiting times by, for example, having the elevator wait on a specific floor during times when residents frequently use it. The standby floor adjustment unit can use AI to dynamically adjust the standby floor based on residents' lifestyle patterns. The visitor handling unit provides priority operation tailored to visitors. The visitor handling unit can, for example, have the elevator wait on the first floor when a visitor arrives, allowing for a smooth welcome for the visitor. The Visitor Handling Unit can use AI to adjust priority operations based on the importance and time of day of visitors. The Cargo Handling Unit adjusts operations when cargo is being delivered. For example, the Cargo Handling Unit adjusts elevator operations when a large amount of cargo is being delivered to avoid affecting other residents' use. The Cargo Handling Unit can use AI to adjust operations based on the type of cargo and delivery time. The Security Enhancement Unit performs access control using facial recognition. For example, the Security Enhancement Unit can recognize residents' faces and restrict elevator use to prevent intruders from entering. The Security Enhancement Unit can use AI to improve the accuracy of facial recognition and optimize the authentication process. The Energy Saving Operation Unit optimizes energy efficiency. For example, the Energy Saving Operation Unit saves energy by automatically turning off air conditioning and lighting during times when the elevator is not in use. The Energy Saving Operation Unit can use AI to reduce power consumption and adjust the operation schedule. As a result, the Smart Elevator Management AI agent according to this embodiment can learn residents' lifestyle patterns and set the optimal waiting floor to optimize elevator operations, improving convenience and efficiency.

[0030] The Lifestyle Rhythm Learning Unit learns the lifestyle patterns of residents. Specifically, it collects data such as residents' wake-up times, return times, and frequency of going out, and learns their lifestyle patterns based on this data. The Lifestyle Rhythm Learning Unit can use AI to analyze residents' lifestyle patterns and provide information to set the optimal waiting floor. The AI ​​uses machine learning algorithms to analyze the collected data and identify residents' behavioral patterns. For example, if a resident wakes up at 7:00 AM every morning and leaves for work at 8:00 AM, the AI ​​learns this pattern and sets the elevator to wait on the resident's floor at 7:50 AM. Also, if a resident frequently goes out on weekends, the AI ​​uses this information to adjust the elevator's waiting floor on weekends. Furthermore, the Lifestyle Rhythm Learning Unit can also respond to changes in residents' behavioral patterns. For example, if a resident's lifestyle pattern changes, the AI ​​learns the new data and updates the waiting floor settings. In this way, the Lifestyle Rhythm Learning Unit can flexibly respond to residents' lifestyle patterns and optimize elevator operation.

[0031] The standby floor adjustment unit sets the optimal standby floor based on the lifestyle patterns learned by the lifestyle rhythm learning unit. Specifically, it reduces waiting times by having the elevator wait on a specific floor during times when residents use it frequently. The standby floor adjustment unit can dynamically adjust the standby floor based on residents' lifestyle patterns using AI. For example, during the morning commute, the elevator can be made to wait on a resident's floor so that residents can use it immediately. Also, at night, the elevator can be made to wait on the first floor so that residents can use it immediately when they return home. Furthermore, the standby floor adjustment unit can also respond to changes in residents' lifestyle patterns. For example, if a resident's lifestyle pattern changes, the AI ​​learns the new data and updates the standby floor setting. In this way, the standby floor adjustment unit can flexibly respond to residents' lifestyle patterns and optimize elevator operation.

[0032] The Visitor Services Department prioritizes elevator operation based on the visitor's needs. Specifically, it will have the elevator wait on the first floor when a visitor arrives, ensuring a smooth arrival. The Visitor Services Department uses AI to adjust priority operation based on the importance and time of the visitor. For example, if an important visitor is arriving, the elevator will be waited on the first floor so that the visitor can use it immediately upon arrival. It will also adjust the elevator operation schedule to match the visitor's arrival time to ensure a smooth journey. Furthermore, by registering visitor information in advance, the Visitor Services Department can automatically have the elevator wait when a visitor arrives. This allows the Visitor Services Department to provide quick and efficient service to visitors.

[0033] The cargo handling department adjusts elevator operations during cargo deliveries. Specifically, it adjusts elevator operations during large-scale cargo deliveries to avoid disrupting other residents' use. The cargo handling department uses AI to adjust operations based on the type of cargo and delivery time. For example, when large cargo is being delivered, it sets the elevator to be exclusively for cargo delivery, allowing other residents to use the elevator. It also adjusts the elevator operation schedule to match the delivery time to avoid disrupting other residents' use. Furthermore, by registering cargo delivery information in advance, the cargo handling department can automatically put the elevator on standby when cargo arrives. This allows the cargo handling department to efficiently adjust operations during cargo deliveries.

[0034] The Security Enhancement Department will implement access control using facial recognition. Specifically, it will recognize residents' faces to restrict elevator use and prevent unauthorized entry. The Security Enhancement Department can use AI to improve the accuracy of facial recognition and optimize the authentication process. For example, by pre-registering residents' faces and performing facial recognition when they use the elevator, use by non-residents will be restricted. In addition, to improve the accuracy of facial recognition, the AI ​​will analyze facial features in detail to enhance recognition accuracy. Furthermore, the Security Enhancement Department can monitor the results of facial recognition in real time and issue an alarm if an unauthorized person is detected. In this way, the Security Enhancement Department can safely manage elevator use and prevent unauthorized entry.

[0035] The Energy-Saving Operations Department optimizes energy efficiency. Specifically, it saves energy by automatically turning off air conditioning and lighting during times when elevators are not in use. The Energy-Saving Operations Department can use AI to reduce power consumption and adjust the operating schedule. For example, if an elevator is not used for a certain period of time, it will automatically turn off the air conditioning and lighting. It also adjusts the elevator operating schedule to optimize energy efficiency. Furthermore, the Energy-Saving Operations Department can collect energy consumption data and analyze consumption patterns using AI to propose further energy-saving measures. As a result, the Energy-Saving Operations Department can improve the energy efficiency of elevators and reduce operating costs.

[0036] The lifestyle rhythm learning unit can learn the lifestyle patterns of residents. For example, the lifestyle rhythm learning unit collects data such as residents' wake-up times, return times, and frequency of going out, and learns lifestyle patterns based on this data. The lifestyle rhythm learning unit can use AI to analyze residents' lifestyle patterns and provide information for setting the optimal waiting floor. Some or all of the above processing in the lifestyle rhythm learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the lifestyle rhythm learning unit can input residents' lifestyle pattern data into a generative AI, which can then analyze the lifestyle patterns and provide information for setting the optimal waiting floor. This makes it possible to optimize elevator operation based on residents' lifestyle patterns, improving convenience and efficiency.

[0037] The standby floor adjustment unit can set the optimal standby floor based on the lifestyle patterns learned by the lifestyle rhythm learning unit. The standby floor adjustment unit can shorten waiting times by, for example, having the elevator wait on a specific floor during times when residents frequently use it. The standby floor adjustment unit can dynamically adjust the standby floor based on the residents' lifestyle patterns using AI. Some or all of the above processing in the standby floor adjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the standby floor adjustment unit can input information provided by the lifestyle rhythm learning unit into the generative AI, which can then set the optimal standby floor. This optimizes the elevator's standby floor based on residents' lifestyle patterns, improving convenience and efficiency.

[0038] The visitor service department can provide priority service tailored to visitors. For example, the visitor service department can have the elevator wait on the first floor when a visitor arrives, ensuring a smooth arrival. The visitor service department can use AI to adjust priority service based on the importance and time of day of the visitor. Some or all of the above processing in the visitor service department may be performed using, for example, a generating AI, or without a generating AI. For example, the visitor service department can input visitor information into a generating AI, which can then adjust priority service. This improves convenience for residents by providing priority service tailored to visitors.

[0039] The cargo handling unit can adjust elevator operations during cargo delivery. For example, the cargo handling unit can adjust elevator operations during large-scale cargo deliveries to avoid affecting other residents. The cargo handling unit can use AI to adjust operations based on the type of cargo and delivery time. Some or all of the above-described processes in the cargo handling unit may be performed using, for example, a generating AI, or without a generating AI. For example, the cargo handling unit can input cargo information into a generating AI, which can then perform the operation adjustments. This allows for improved convenience for residents by adjusting operations during cargo delivery.

[0040] The security enhancement unit can perform access control using facial recognition. For example, the security enhancement unit can recognize residents' faces and restrict elevator use to prevent intruders from entering. The security enhancement unit can use AI to improve the accuracy of facial recognition and optimize the authentication process. Some or all of the above processing in the security enhancement unit may be performed using, for example, generative AI, or without generative AI. For example, the security enhancement unit can input residents' facial data into generative AI, which will perform facial recognition and implement access control. This strengthens security measures and improves the safety of residents by implementing access control using facial recognition.

[0041] The energy-saving operation unit can optimize energy efficiency. For example, the energy-saving operation unit saves energy by automatically turning off air conditioning and lighting during times when the elevator is not in use. The energy-saving operation unit can use AI to adjust methods for reducing power consumption and the operation schedule. Some or all of the above processes in the energy-saving operation unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the energy-saving operation unit can input elevator operation data into a generating AI, which can then adjust the operation schedule to optimize energy efficiency. This can optimize energy efficiency and reduce elevator operating costs.

[0042] The notification unit can notify users of waiting times and congestion levels in real time via a smartphone app. For example, residents can check the current waiting times and congestion levels on the app before using the elevator, allowing them to use the elevator efficiently. The notification unit can use AI to analyze waiting times and congestion levels in real time and notify residents. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input elevator operation data into a generative AI, which can analyze waiting times and congestion levels and notify residents in real time. This allows residents to understand elevator usage and use the elevator efficiently.

[0043] The lifestyle rhythm learning unit can analyze residents' past behavioral history and predict changes in their lifestyle rhythm. For example, the lifestyle rhythm learning unit can analyze the time periods that residents frequently used in the past and adjust the elevator waiting floors during those times. The lifestyle rhythm learning unit can adjust the elevator waiting floors during specific events or occasions based on residents' past behavioral history. The lifestyle rhythm learning unit can analyze residents' behavioral patterns and adjust the elevator waiting floors according to seasonal and weather changes. Some or all of the above processing in the lifestyle rhythm learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the lifestyle rhythm learning unit can input residents' behavioral history data into a generative AI, which can analyze the behavioral history and predict changes in their lifestyle rhythm. This allows for the prediction of changes in lifestyle rhythms and the optimization of elevator operation by analyzing residents' past behavioral history.

[0044] The lifestyle rhythm learning unit can dynamically adjust the elevator's waiting floor based on the residents' lifestyle rhythms. For example, if a resident frequently uses the elevator during the morning commute, the lifestyle rhythm learning unit can set the elevator's waiting floor to the first floor. If a resident returns home at night, the lifestyle rhythm learning unit can set the elevator's waiting floor to an upper floor. If a resident frequently uses the elevator on holidays, the lifestyle rhythm learning unit can adjust the elevator's waiting floor to coincide with that time. Some or all of the above processing in the lifestyle rhythm learning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the lifestyle rhythm learning unit can input residents' lifestyle rhythm data into a generating AI, which can then dynamically adjust the waiting floor. This allows for improved convenience by dynamically adjusting the elevator's waiting floor based on the residents' lifestyle rhythms.

[0045] The lifestyle rhythm learning unit can optimize the elevator operation schedule based on the residents' lifestyle rhythms. For example, the lifestyle rhythm learning unit can adjust the elevator operation schedule to match the residents' commuting times. The lifestyle rhythm learning unit can adjust the elevator operation schedule to match the residents' returning home times. The lifestyle rhythm learning unit can adjust the elevator operation schedule to match the residents' holiday activity patterns. Some or all of the above processing in the lifestyle rhythm learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the lifestyle rhythm learning unit can input residents' lifestyle rhythm data into a generative AI, which can then optimize the operation schedule. This improves convenience by optimizing the elevator operation schedule based on the residents' lifestyle rhythms.

[0046] The lifestyle rhythm learning unit can adjust the elevator maintenance schedule based on the residents' lifestyle rhythms. For example, the lifestyle rhythm learning unit can perform maintenance during times when residents are not using the elevator. The lifestyle rhythm learning unit can adjust the frequency of maintenance to match the residents' lifestyle rhythms. The lifestyle rhythm learning unit can analyze residents' behavior patterns and set an optimal maintenance schedule. Some or all of the above processes in the lifestyle rhythm learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the lifestyle rhythm learning unit can input residents' lifestyle rhythm data into a generative AI, which can then adjust the maintenance schedule. This improves convenience by adjusting the elevator maintenance schedule based on the residents' lifestyle rhythms.

[0047] The standby floor adjustment unit can select the optimal standby floor by referring to the resident's past usage history when setting the standby floor. For example, the standby floor adjustment unit can set a floor that the resident has frequently used in the past as the standby floor. The standby floor adjustment unit can select the optimal standby floor for a specific time period based on the resident's past usage history. The standby floor adjustment unit can analyze the resident's behavior patterns and select the optimal standby floor. Some or all of the above processing in the standby floor adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the standby floor adjustment unit can input the resident's usage history data into a generating AI, which can analyze the usage history and select the optimal standby floor. This improves convenience by selecting the optimal standby floor by referring to the resident's past usage history.

[0048] The standby floor adjustment unit can dynamically change the standby floor when setting the standby floor, taking into account the current living situation of the residents. For example, if a resident is in a hurry, the standby floor adjustment unit can set the elevator standby floor to the first floor. If a resident is relaxed, the standby floor adjustment unit can set the elevator standby floor to the entrance. If a resident is carrying luggage, the standby floor adjustment unit can set the elevator standby floor to a floor close to the resident's room. Some or all of the above processing in the standby floor adjustment unit may be performed using, for example, a generating AI, or without a generating AI. For example, the standby floor adjustment unit can input resident living situation data into a generating AI, which can analyze the living situation and dynamically change the standby floor. This improves convenience by dynamically changing the standby floor considering the current living situation of the residents.

[0049] The standby floor adjustment unit can select the optimal standby floor when setting the standby floor, taking into account the resident's geographical location information. For example, if the resident is at their current location, the standby floor adjustment unit can set the elevator standby floor to a floor close to the resident's room. If the resident is in the entrance, the standby floor adjustment unit can set the elevator standby floor to the first floor. If the resident is on a common floor, the standby floor adjustment unit can set the elevator standby floor to the common floor. Some or all of the above processing in the standby floor adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the standby floor adjustment unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal standby floor. This improves convenience by selecting the optimal standby floor considering the resident's geographical location information.

[0050] The standby floor adjustment unit can adjust the standby floor by analyzing residents' social media activity when setting the standby floor. For example, if a resident posts their current location on social media, the standby floor of the elevator can be set to that location. If a resident participates in an event on social media, the standby floor of the elevator can be set to the event venue. If a resident indicates on social media that they are in a specific location, the standby floor of the elevator can be set to that location. Some or all of the above processing in the standby floor adjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the standby floor adjustment unit can input resident social media activity data into a generative AI, which can analyze the social media activity and adjust the standby floor. This improves convenience by analyzing residents' social media activity and adjusting the standby floor.

[0051] The visitor response unit can select the optimal response method by referring to the resident's past visitor history when responding to visitors. For example, the visitor response unit can adjust the elevator waiting floor to match the time of day when the resident frequently received visitors in the past. The visitor response unit can adjust the elevator waiting floor for specific events or occasions based on the resident's past visitor history. The visitor response unit can analyze the resident's visitor patterns and select the optimal response method. Some or all of the above processes in the visitor response unit may be performed using, for example, a generating AI, or without a generating AI. For example, the visitor response unit can input the resident's visitor history data into a generating AI, which can analyze the visitor history and select the optimal response method. This improves convenience by selecting the optimal response method by referring to the resident's past visitor history.

[0052] The visitor reception department can customize its response methods when receiving visitors, taking into account the resident's current living situation. For example, if a resident is in a hurry, the visitor reception department can respond quickly, saving the resident's time. If a resident is relaxed, the visitor reception department can respond carefully, improving the resident's satisfaction. If a resident is carrying luggage, the visitor reception department can respond quickly, reducing the resident's burden. Some or all of the above processes in the visitor reception department may be performed using, for example, a generative AI, or not using a generative AI. For example, the visitor reception department can input data on the resident's living situation into a generative AI, which can then analyze the situation and customize the response method. This improves convenience by customizing the response method to take into account the resident's current living situation.

[0053] The visitor reception department can select the optimal response method when receiving visitors, taking into account the resident's geographical location information. For example, if the resident is at their current location, the visitor reception department can respond quickly, reducing the resident's burden. If the resident is at the entrance, the visitor reception department can respond quickly, saving the resident's time. If the resident is on a common floor, the visitor reception department can respond quickly, improving resident satisfaction. Some or all of the above processing in the visitor reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the visitor reception department can input the resident's geographical location information into a generative AI, which can analyze the geographical location information and select the optimal response method. This improves convenience by selecting the optimal response method considering the resident's geographical location information.

[0054] The visitor service department can analyze residents' social media activity and adjust its response methods when handling visitors. For example, if a resident posts about an upcoming visit on social media, the visitor service department can adjust the elevator's waiting floor to coincide with that time. If a resident is participating in an event on social media, the visitor service department can provide prompt assistance, reducing the burden on the resident. If a resident indicates on social media that they are in a specific location, the visitor service department can provide prompt assistance, improving resident satisfaction. Some or all of the above processes in the visitor service department may be performed using, for example, a generative AI, or not. For example, the visitor service department can input resident social media activity data into a generative AI, which can then analyze the social media activity and adjust its response methods. This allows for improved convenience by analyzing residents' social media activity and adjusting response methods accordingly.

[0055] The cargo handling unit can select the optimal handling method by referring to the resident's past cargo delivery history when handling cargo. For example, the cargo handling unit can adjust the elevator waiting floor to match the time of day when the resident frequently delivered cargo in the past. The cargo handling unit can adjust the elevator waiting floor during specific events or occasions based on the resident's past cargo delivery history. The cargo handling unit can analyze the resident's cargo delivery patterns and select the optimal handling method. Some or all of the above processing in the cargo handling unit may be performed using, for example, a generating AI, or without a generating AI. For example, the cargo handling unit can input the resident's cargo delivery history data into a generating AI, which can analyze the cargo delivery history and select the optimal handling method. This improves convenience by selecting the optimal handling method by referring to the resident's past cargo delivery history.

[0056] The package handling unit can customize its handling methods when handling packages, taking into account the resident's current living situation. For example, if a resident is in a hurry, the package handling unit can handle the package quickly, saving the resident time. If a resident is relaxed, the package handling unit can handle the package carefully, improving the resident's satisfaction. If a resident is carrying a package, the package handling unit can handle the package quickly, reducing the resident's burden. Some or all of the above processes in the package handling unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the package handling unit can input resident living situation data into a generative AI, which can analyze the living situation and customize the handling method. This can improve convenience by customizing the handling method to take into account the resident's current living situation.

[0057] The package handling unit can select the optimal handling method when handling packages, taking into account the resident's geographical location information. For example, if the resident is at their current location, the package handling unit can handle the package quickly, reducing the resident's burden. If the resident is at the entrance, the package handling unit can handle the package quickly, saving the resident's time. If the resident is on a common floor, the package handling unit can handle the package quickly, improving resident satisfaction. Some or all of the above processing in the package handling unit may be performed using, for example, a generating AI, or without a generating AI. For example, the package handling unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal handling method. This improves convenience by selecting the optimal handling method considering the resident's geographical location information.

[0058] The package handling unit can analyze residents' social media activity and adjust its handling methods when handling packages. For example, if a resident posts on social media about the expected arrival date of a package, the package handling unit can adjust the elevator waiting floor to that time. If a resident is participating in an event on social media, the package handling unit can expedite the package handling to reduce the resident's burden. If a resident indicates on social media that they are in a specific location, the package handling unit can expedite the package handling to improve resident satisfaction. Some or all of the above processing in the package handling unit may be performed using, for example, generative AI, or not using generative AI. For example, the package handling unit can input resident social media activity data into generative AI, which can analyze the social media activity and adjust the handling method. This can improve convenience by analyzing residents' social media activity and adjusting the handling method accordingly.

[0059] The Crime Prevention Enhancement Department can select the most appropriate crime prevention method by referring to residents' past crime prevention history when enhancing crime prevention measures. For example, the Crime Prevention Enhancement Department can adjust crime prevention measures to match the time periods in which residents have frequently taken extra precautions in the past. The Crime Prevention Enhancement Department can adjust crime prevention measures for specific events or occasions based on residents' past crime prevention history. The Crime Prevention Enhancement Department can analyze residents' crime prevention patterns and select the most appropriate crime prevention method. Some or all of the above processes in the Crime Prevention Enhancement Department may be performed using, for example, a generating AI, or without a generating AI. For example, the Crime Prevention Enhancement Department can input residents' crime prevention history data into a generating AI, which can then analyze the history and select the most appropriate crime prevention method. This allows for an improvement in residents' sense of security by selecting the most appropriate crime prevention method based on their past crime prevention history.

[0060] The crime prevention enhancement department can customize crime prevention methods when enhancing crime prevention, taking into account the current living situation of residents. For example, if a resident is in a hurry, the crime prevention enhancement department can quickly enhance crime prevention to save the resident's time. If a resident is relaxed, the crime prevention enhancement department can carefully enhance crime prevention to improve the resident's satisfaction. If a resident is carrying luggage, the crime prevention enhancement department can quickly enhance crime prevention to reduce the resident's burden. Some or all of the above processes in the crime prevention enhancement department may be performed using, for example, a generating AI, or not using a generating AI. For example, the crime prevention enhancement department can input data on the resident's living situation into a generating AI, which can analyze the living situation and customize crime prevention methods. This can improve the sense of security by customizing crime prevention methods to take into account the resident's current living situation.

[0061] The Security Enhancement Department can select the optimal security method when enhancing security, taking into account the geographical location information of residents. For example, if a resident is at their current location, the Security Enhancement Department can quickly implement security enhancements to increase the resident's sense of security. If a resident is at the entrance, the Security Enhancement Department can quickly implement security enhancements to save the resident's time. If a resident is on a common floor, the Security Enhancement Department can quickly implement security enhancements to improve resident satisfaction. Some or all of the above processing in the Security Enhancement Department may be performed using, for example, a generating AI, or without using a generating AI. For example, the Security Enhancement Department can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal security method. This allows for the selection of the optimal security method while considering the resident's geographical location information, thereby improving their sense of security.

[0062] The Crime Prevention Enhancement Department can analyze residents' social media activity and adjust crime prevention methods when enhancing crime prevention. For example, if a resident posts on social media about the need for crime prevention, the Crime Prevention Enhancement Department can quickly implement enhanced crime prevention measures to increase residents' sense of security. If a resident participates in an event on social media, the Crime Prevention Enhancement Department can quickly implement enhanced crime prevention measures to reduce the burden on residents. If a resident indicates on social media that they are in a specific location, the Crime Prevention Enhancement Department can quickly implement enhanced crime prevention measures to improve residents' satisfaction. Some or all of the above processes in the Crime Prevention Enhancement Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Crime Prevention Enhancement Department can input residents' social media activity data into a generative AI, which can analyze the social media activity and adjust crime prevention methods. This allows for an improvement in residents' sense of security by analyzing their social media activity and adjusting crime prevention methods accordingly.

[0063] The energy-saving operation unit can select the optimal energy-saving method by referring to residents' past energy consumption history during energy-saving operation. For example, the energy-saving operation unit can adjust energy-saving operation to match the time periods when residents frequently consumed energy in the past. The energy-saving operation unit can adjust energy-saving operation during specific events or occasions based on residents' past energy consumption history. The energy-saving operation unit can analyze residents' energy consumption patterns and select the optimal energy-saving method. Some or all of the above processes in the energy-saving operation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the energy-saving operation unit can input residents' energy consumption history data into a generating AI, which can then analyze the energy consumption history and select the optimal energy-saving method. This allows for improved energy efficiency by selecting the optimal energy-saving method by referring to residents' past energy consumption history.

[0064] The energy-saving operation unit can customize energy-saving methods during energy-saving operation, taking into account the current living situation of residents. For example, if residents are in a hurry, the energy-saving operation unit can perform energy-saving operations quickly to save residents time. If residents are relaxed, the energy-saving operation unit can perform energy-saving operations carefully to improve residents' satisfaction. If residents are carrying luggage, the energy-saving operation unit can perform energy-saving operations quickly to reduce the burden on residents. Some or all of the above processes in the energy-saving operation unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the energy-saving operation unit can input residents' living situation data into a generating AI, which can analyze the living situation and customize energy-saving methods. This allows for improved energy efficiency by customizing energy-saving methods to take into account the current living situation of residents.

[0065] The energy-saving operation unit can select the optimal energy-saving method by considering the geographical location information of residents during energy-saving operation. For example, if a resident is at their current location, the energy-saving operation unit can quickly implement energy-saving operation to reduce the burden on the resident. If a resident is in the entrance, the energy-saving operation unit can quickly implement energy-saving operation to save the resident's time. If a resident is on a common floor, the energy-saving operation unit can quickly implement energy-saving operation to improve resident satisfaction. Some or all of the above processing in the energy-saving operation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the energy-saving operation unit can input the geographical location information of residents into a generating AI, which can analyze the geographical location information and select the optimal energy-saving method. This allows for improved energy efficiency by selecting the optimal energy-saving method by considering the geographical location information of residents.

[0066] The Energy-Saving Operations Department can analyze residents' social media activity during energy-saving operations and adjust energy-saving methods accordingly. For example, if a resident posts about the need for energy saving on social media, the Energy-Saving Operations Department can quickly implement energy-saving operations to reduce the burden on the resident. If a resident participates in an event on social media, the Energy-Saving Operations Department can quickly implement energy-saving operations to reduce the burden on the resident. If a resident indicates on social media that they are in a specific location, the Energy-Saving Operations Department can quickly implement energy-saving operations to improve resident satisfaction. Some or all of the above processing in the Energy-Saving Operations Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Energy-Saving Operations Department can input resident social media activity data into a generative AI, which can then analyze the social media activity and adjust energy-saving methods. This allows for improved energy efficiency by analyzing residents' social media activity and adjusting energy-saving methods accordingly.

[0067] The assistance unit can select the optimal assistance method by referring to the resident's past usage history when providing assistance functions. For example, the assistance unit can prioritize providing assistance functions that the resident has frequently used in the past. The assistance unit can select the optimal assistance method for a specific time period based on the resident's past usage history. The assistance unit can analyze the resident's usage patterns and select the optimal assistance method. Some or all of the above processing in the assistance unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the assistance unit can input the resident's usage history data into a generating AI, which can then analyze the usage history and select the optimal assistance method. This improves convenience by selecting the optimal assistance method by referring to the resident's past usage history.

[0068] The assistance unit can select the optimal assistance method by considering the resident's geographical location information when providing assistance functions. For example, if the resident is at their current location, the assistance unit can quickly provide assistance functions to reduce the resident's burden. If the resident is at the entrance, the assistance unit can quickly provide assistance functions to save the resident's time. If the resident is on a shared floor, the assistance unit can quickly provide assistance functions to improve resident satisfaction. Some or all of the above processing in the assistance unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the assistance unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal assistance method. This improves convenience by selecting the optimal assistance method by considering the resident's geographical location information.

[0069] The settings unit can select the optimal settings method by referring to the resident's past settings history during the settings process. For example, the settings unit may prioritize providing settings that the resident has frequently used in the past. The settings unit can select the optimal settings method for a specific time period based on the resident's past settings history. The settings unit can analyze the resident's settings patterns and select the optimal settings method. Some or all of the above-described processes in the settings unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the settings unit can input the resident's settings history data into a generating AI, which can then analyze the settings history and select the optimal settings method. This improves usability by selecting the optimal settings method by referring to the resident's past settings history.

[0070] The configuration unit can select the optimal configuration method by considering the resident's geographical location information during configuration. For example, if the resident is at their current location, the configuration unit can quickly configure the system, reducing the burden on the resident. If the resident is at the entrance, the configuration unit can quickly configure the system, saving the resident time. If the resident is on a shared floor, the configuration unit can quickly configure the system, improving resident satisfaction. Some or all of the above-described processes in the configuration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the configuration unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal configuration method. This improves convenience by selecting the optimal configuration method by considering the resident's geographical location information.

[0071] The transportation planning department can select the optimal transportation plan by referring to residents' past transportation history when planning transportation. For example, the transportation planning department can adjust the transportation plan to match the time slots that residents have frequently used in the past. The transportation planning department can adjust the transportation plan for specific events or occasions based on residents' past transportation history. The transportation planning department can analyze residents' transportation patterns and select the optimal transportation plan. Some or all of the above processes in the transportation planning department may be performed using, for example, a generating AI, or not using a generating AI. For example, the transportation planning department can input residents' transportation history data into a generating AI, which can analyze the transportation history and select the optimal transportation plan. This improves convenience by selecting the optimal transportation plan by referring to residents' past transportation history.

[0072] The transportation planning department can select the optimal transportation plan when planning transportation, taking into account the geographical location information of residents. For example, if a resident is at their current location, the transportation planning department can quickly create a transportation plan, reducing the burden on the resident. If a resident is at the entrance, the transportation planning department can quickly create a transportation plan, saving the resident time. If a resident is on a common floor, the transportation planning department can quickly create a transportation plan, improving resident satisfaction. Some or all of the above processing in the transportation planning department may be performed using, for example, a generating AI, or without a generating AI. For example, the transportation planning department can input the geographical location information of residents into a generating AI, which can analyze the geographical location information and select the optimal transportation plan. This improves convenience by selecting the optimal transportation plan while taking into account the geographical location information of residents.

[0073] The security mode unit can select the optimal security method by referring to the resident's past security history when security mode is activated. For example, the security mode unit can adjust the security mode to match the time period in which the resident frequently activated security mode in the past. The security mode unit can adjust the security mode for specific events or occasions based on the resident's past security history. The security mode unit can analyze the resident's security patterns and select the optimal security method. Some or all of the above processing in the security mode unit may be performed using, for example, a generating AI, or without a generating AI. For example, the security mode unit can input the resident's security history data into a generating AI, which can analyze the security history and select the optimal security method. This can improve the sense of security by selecting the optimal security method by referring to the resident's past security history.

[0074] The security mode unit can customize security methods when implementing security mode, taking into account the current living situation of the residents. For example, if a resident is in a hurry, the security mode unit can quickly implement security mode to save the resident's time. If a resident is relaxed, the security mode unit can carefully implement security mode to improve resident satisfaction. If a resident is carrying luggage, the security mode unit can quickly implement security mode to reduce the resident's burden. Some or all of the above processing in the security mode unit may be performed using, for example, a generating AI, or without a generating AI. For example, the security mode unit can input resident living situation data into a generating AI, which can analyze the living situation and customize security methods. This can improve the sense of security by customizing security methods to take into account the current living situation of the residents.

[0075] The security mode unit can select the optimal security method by considering the geographical location information of residents when implementing security mode. For example, if a resident is at their current location, the security mode unit can quickly activate security mode to enhance the resident's sense of security. If a resident is at the entrance, the security mode unit can quickly activate security mode to save the resident's time. If a resident is on a shared floor, the security mode unit can quickly activate security mode to improve resident satisfaction. Some or all of the above processing in the security mode unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the security mode unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal security method. This allows for an improvement in the resident's sense of security by selecting the optimal security method by considering their geographical location information.

[0076] The security mode unit can analyze residents' social media activity and adjust security methods when security mode is implemented. For example, if a resident posts on social media that they need security, the security mode unit can quickly activate security mode to increase the resident's sense of security. If a resident participates in an event on social media, the security mode unit can quickly activate security mode to reduce the resident's burden. If a resident indicates on social media that they are in a specific location, the security mode unit can quickly activate security mode to improve resident satisfaction. Some or all of the above processing in the security mode unit may be performed using, for example, a generative AI, or without a generative AI. For example, the security mode unit can input resident social media activity data into a generative AI, which can analyze the social media activity and adjust security methods. This allows for an improvement in the resident's sense of security by analyzing their social media activity and adjusting security methods accordingly.

[0077] The adjustment unit can select the optimal adjustment method by referring to the resident's past adjustment history during the adjustment process. For example, the adjustment unit may prioritize providing adjustments that the resident has frequently performed in the past. The adjustment unit can select the optimal adjustment method for a specific time period based on the resident's past adjustment history. The adjustment unit can analyze the resident's adjustment patterns and select the optimal adjustment method. Some or all of the above processes in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit can input the resident's adjustment history data into a generating AI, which can then analyze the adjustment history and select the optimal adjustment method. This improves convenience by selecting the optimal adjustment method by referring to the resident's past adjustment history.

[0078] The adjustment unit can select the optimal adjustment method by considering the geographical location information of residents during the adjustment process. For example, if a resident is at their current location, the adjustment unit can perform the adjustment quickly, reducing the burden on the resident. If a resident is at the entrance, the adjustment unit can perform the adjustment quickly, saving the resident time. If a resident is on a common floor, the adjustment unit can perform the adjustment quickly, improving resident satisfaction. Some or all of the above-described processes in the adjustment unit may be performed using, for example, a generating AI, or without a generating AI. For example, the adjustment unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal adjustment method. This improves convenience by selecting the optimal adjustment method by considering the resident's geographical location information.

[0079] The notification unit can select the optimal notification method by referring to the resident's past notification history when sending a notification. For example, the notification unit can prioritize providing notifications that the resident has frequently received in the past. The notification unit can select the optimal notification method for a specific time period based on the resident's past notification history. The notification unit can analyze the resident's notification patterns and select the optimal notification method. Some or all of the above processing in the notification unit may be performed using, for example, a generation AI, or without a generation AI. For example, the notification unit can input the resident's notification history data into a generation AI, which can then analyze the notification history and select the optimal notification method. This improves convenience by selecting the optimal notification method by referring to the resident's past notification history.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The smart elevator management AI agent can monitor residents' health status and operate elevators accordingly. For example, if a resident is feeling unwell, the elevator waiting floor can be set to a floor close to the resident's room to minimize travel. If a resident is not getting enough exercise, the elevator waiting floor can be set to a floor slightly further away to encourage walking. Furthermore, if a resident is injured, the elevator waiting floor can be set to the floor closest to the resident's room to support quick relocation. In this way, by operating elevators according to residents' health status, the system can support their well-being.

[0082] The smart elevator management AI agent can customize the elevator environment based on the resident's hobbies and preferences. For example, if a resident likes music, the elevator can play their favorite music. If a resident likes a particular scent, that scent can be diffused inside the elevator. Furthermore, if a resident prefers a specific type of lighting, the elevator's lighting can be adjusted to suit that resident's preference. In this way, by customizing the elevator environment based on the resident's hobbies and preferences, a more comfortable travel experience can be provided.

[0083] The smart elevator management AI agent can analyze residents' past elevator usage history and optimize elevator operation based on that history. For example, it can reduce waiting times by having elevators wait during times when residents frequently used them in the past. It can also support quicker travel by having elevators wait on floors that residents have used in the past. Furthermore, it can analyze specific patterns from residents' usage history and adjust elevator operation based on those patterns. In this way, convenience can be improved by optimizing elevator operation based on residents' past usage history.

[0084] The smart elevator management AI agent can optimize elevator operation using residents' geographical location information. For example, if a resident is currently at a location, the elevator can be made to wait at that location to support quick movement. Similarly, if a resident is at the entrance, the elevator can be made to wait on the first floor to support quick movement. Furthermore, if a resident is on a common floor, the elevator can be made to wait on that floor to support quick movement. In this way, by optimizing elevator operation using residents' geographical location information, convenience can be improved.

[0085] The smart elevator management AI agent can analyze residents' social media activity and optimize elevator operations. For example, if a resident posts their current location on social media, the elevator can be made to wait at that location to support quick travel. Similarly, if a resident participates in an event on social media, the elevator can be made to wait at the event venue to support quick travel. Furthermore, if a resident indicates on social media that they are in a specific location, the elevator can be made to wait at that location to support quick travel. In this way, by analyzing residents' social media activity and optimizing elevator operations, convenience can be improved.

[0086] The smart elevator management AI agent can analyze residents' past elevator usage history and optimize elevator operation based on that history. For example, it can reduce waiting times by having elevators wait during times when residents frequently used them in the past. It can also support quicker travel by having elevators wait on floors that residents have used in the past. Furthermore, it can analyze specific patterns from residents' usage history and adjust elevator operation based on those patterns. In this way, convenience can be improved by optimizing elevator operation based on residents' past usage history.

[0087] The following briefly describes the processing flow for example form 1.

[0088] Step 1: The lifestyle rhythm learning unit learns the residents' lifestyle patterns. For example, it collects data such as residents' wake-up times, return times, and frequency of going out, and learns lifestyle patterns based on this data. Using AI, it analyzes the residents' lifestyle patterns and provides information to set the optimal waiting floor. Step 2: The standby floor adjustment unit sets the optimal standby floor based on the lifestyle patterns learned by the lifestyle rhythm learning unit. For example, by having the elevator wait on a specific floor during times when residents frequently use it, waiting times are reduced. AI is used to dynamically adjust the standby floor based on residents' lifestyle patterns. Step 3: The visitor service department prioritizes elevator operation based on the visitor's needs. For example, when a visitor arrives, the elevator will be made to wait on the first floor to ensure a smooth arrival. AI is used to adjust priority operation based on the importance and time of day of the visitor. Step 4: The cargo handling department adjusts operations during cargo delivery. For example, it adjusts elevator operations during large-scale cargo deliveries to avoid disrupting other residents' use. AI is used to adjust operations based on the type of cargo and delivery time. Step 5: The security enhancement department implements access control using facial recognition. For example, it recognizes residents' faces to restrict elevator use and prevent intruders from entering. AI is used to improve the accuracy of facial recognition and optimize the authentication process. Step 6: The energy-saving operations department optimizes energy efficiency. For example, it saves energy by automatically turning off air conditioning and lighting during times when elevators are not in use. AI is used to reduce power consumption and adjust the operation schedule.

[0089] (Example of form 2) The Smart Elevator Management AI Agent according to an embodiment of the present invention is a system that innovates elevator systems in high-rise condominiums and improves the comfort and safety of residents. This system learns the lifestyle patterns of residents and sets the optimal waiting floor, so that elevators are waiting during times when residents use them most frequently, thus reducing waiting times. For example, during the morning rush hour, the elevator will be waiting on the first floor so that residents can use it immediately. Next, it supports priority operation for visitors and operational adjustments when loading and unloading goods. For example, when a visitor arrives, the elevator will automatically wait on the first floor to welcome the visitor smoothly. Also, when loading and unloading large quantities of goods, the elevator operation will be adjusted to avoid affecting the use of other residents. Furthermore, it provides support functions for the elderly and people with disabilities and settings for preferred shared floors. For example, when an elderly person uses the elevator, the elevator will operate slowly to allow for smooth boarding and alighting. Also, by setting the shared floor, the elevator will automatically wait on the floor preferred by the resident. It performs optimal operation according to the rush hour and return-home times. For example, during the morning rush hour, the elevator will be waiting on the first floor so that residents can use it immediately. Furthermore, during rush hour, elevators will wait on upper floors to ensure residents can return home smoothly. Access control using facial recognition and a nighttime security mode will be implemented. For example, by recognizing residents' faces, elevator use will be restricted to prevent intruders. In addition, security mode will be implemented at night to restrict elevator use and enhance crime prevention measures. Energy efficiency will be optimized, and air conditioning and lighting will be automatically adjusted. For example, air conditioning and lighting will be automatically turned off during times when the elevators are not in use to save energy. Waiting times and congestion status will be notified in real time via a smartphone app. For example, residents can check the current waiting time and congestion status on the app before using the elevator, allowing them to use the elevator efficiently. In this way, the smart elevator management AI agent will improve resident convenience and elevator efficiency, and provide a sustainable living environment by enhancing security and energy saving.Furthermore, it can provide a stress-free lifestyle with smooth boarding and alighting. This allows the smart elevator management AI agent to learn residents' lifestyle patterns and set optimal waiting floors, thereby optimizing elevator operation and improving convenience and efficiency.

[0090] The smart elevator management AI agent according to this embodiment includes a lifestyle rhythm learning unit, a standby floor adjustment unit, a visitor handling unit, a luggage handling unit, a security enhancement unit, and an energy-saving operation unit. The lifestyle rhythm learning unit learns the lifestyle patterns of residents. The lifestyle rhythm learning unit collects data such as residents' wake-up times, return-home times, and frequency of going out, and learns lifestyle patterns based on this data. The lifestyle rhythm learning unit can use AI to analyze residents' lifestyle patterns and provide information for setting the optimal standby floor. The standby floor adjustment unit sets the optimal standby floor based on the lifestyle patterns learned by the lifestyle rhythm learning unit. The standby floor adjustment unit shortens waiting times by, for example, having the elevator wait on a specific floor during times when residents frequently use it. The standby floor adjustment unit can use AI to dynamically adjust the standby floor based on residents' lifestyle patterns. The visitor handling unit provides priority operation tailored to visitors. The visitor handling unit can, for example, have the elevator wait on the first floor when a visitor arrives, allowing for a smooth welcome for the visitor. The Visitor Handling Unit can use AI to adjust priority operations based on the importance and time of day of visitors. The Cargo Handling Unit adjusts operations when cargo is being delivered. For example, the Cargo Handling Unit adjusts elevator operations when a large amount of cargo is being delivered to avoid affecting other residents' use. The Cargo Handling Unit can use AI to adjust operations based on the type of cargo and delivery time. The Security Enhancement Unit performs access control using facial recognition. For example, the Security Enhancement Unit can recognize residents' faces and restrict elevator use to prevent intruders from entering. The Security Enhancement Unit can use AI to improve the accuracy of facial recognition and optimize the authentication process. The Energy Saving Operation Unit optimizes energy efficiency. For example, the Energy Saving Operation Unit saves energy by automatically turning off air conditioning and lighting during times when the elevator is not in use. The Energy Saving Operation Unit can use AI to reduce power consumption and adjust the operation schedule. As a result, the Smart Elevator Management AI agent according to this embodiment can learn residents' lifestyle patterns and set the optimal waiting floor to optimize elevator operations, improving convenience and efficiency.

[0091] The Lifestyle Rhythm Learning Unit learns the lifestyle patterns of residents. Specifically, it collects data such as residents' wake-up times, return times, and frequency of going out, and learns their lifestyle patterns based on this data. The Lifestyle Rhythm Learning Unit can use AI to analyze residents' lifestyle patterns and provide information to set the optimal waiting floor. The AI ​​uses machine learning algorithms to analyze the collected data and identify residents' behavioral patterns. For example, if a resident wakes up at 7:00 AM every morning and leaves for work at 8:00 AM, the AI ​​learns this pattern and sets the elevator to wait on the resident's floor at 7:50 AM. Also, if a resident frequently goes out on weekends, the AI ​​uses this information to adjust the elevator's waiting floor on weekends. Furthermore, the Lifestyle Rhythm Learning Unit can also respond to changes in residents' behavioral patterns. For example, if a resident's lifestyle pattern changes, the AI ​​learns the new data and updates the waiting floor settings. In this way, the Lifestyle Rhythm Learning Unit can flexibly respond to residents' lifestyle patterns and optimize elevator operation.

[0092] The standby floor adjustment unit sets the optimal standby floor based on the lifestyle patterns learned by the lifestyle rhythm learning unit. Specifically, it reduces waiting times by having the elevator wait on a specific floor during times when residents use it frequently. The standby floor adjustment unit can dynamically adjust the standby floor based on residents' lifestyle patterns using AI. For example, during the morning commute, the elevator can be made to wait on a resident's floor so that residents can use it immediately. Also, at night, the elevator can be made to wait on the first floor so that residents can use it immediately when they return home. Furthermore, the standby floor adjustment unit can also respond to changes in residents' lifestyle patterns. For example, if a resident's lifestyle pattern changes, the AI ​​learns the new data and updates the standby floor setting. In this way, the standby floor adjustment unit can flexibly respond to residents' lifestyle patterns and optimize elevator operation.

[0093] The Visitor Services Department prioritizes elevator operation based on the visitor's needs. Specifically, it will have the elevator wait on the first floor when a visitor arrives, ensuring a smooth arrival. The Visitor Services Department uses AI to adjust priority operation based on the importance and time of the visitor. For example, if an important visitor is arriving, the elevator will be waited on the first floor so that the visitor can use it immediately upon arrival. It will also adjust the elevator operation schedule to match the visitor's arrival time to ensure a smooth journey. Furthermore, by registering visitor information in advance, the Visitor Services Department can automatically have the elevator wait when a visitor arrives. This allows the Visitor Services Department to provide quick and efficient service to visitors.

[0094] The cargo handling department adjusts elevator operations during cargo deliveries. Specifically, it adjusts elevator operations during large-scale cargo deliveries to avoid disrupting other residents' use. The cargo handling department uses AI to adjust operations based on the type of cargo and delivery time. For example, when large cargo is being delivered, it sets the elevator to be exclusively for cargo delivery, allowing other residents to use the elevator. It also adjusts the elevator operation schedule to match the delivery time to avoid disrupting other residents' use. Furthermore, by registering cargo delivery information in advance, the cargo handling department can automatically put the elevator on standby when cargo arrives. This allows the cargo handling department to efficiently adjust operations during cargo deliveries.

[0095] The Security Enhancement Department will implement access control using facial recognition. Specifically, it will recognize residents' faces to restrict elevator use and prevent unauthorized entry. The Security Enhancement Department can use AI to improve the accuracy of facial recognition and optimize the authentication process. For example, by pre-registering residents' faces and performing facial recognition when they use the elevator, use by non-residents will be restricted. In addition, to improve the accuracy of facial recognition, the AI ​​will analyze facial features in detail to enhance recognition accuracy. Furthermore, the Security Enhancement Department can monitor the results of facial recognition in real time and issue an alarm if an unauthorized person is detected. In this way, the Security Enhancement Department can safely manage elevator use and prevent unauthorized entry.

[0096] The Energy-Saving Operations Department optimizes energy efficiency. Specifically, it saves energy by automatically turning off air conditioning and lighting during times when elevators are not in use. The Energy-Saving Operations Department can use AI to reduce power consumption and adjust the operating schedule. For example, if an elevator is not used for a certain period of time, it will automatically turn off the air conditioning and lighting. It also adjusts the elevator operating schedule to optimize energy efficiency. Furthermore, the Energy-Saving Operations Department can collect energy consumption data and analyze consumption patterns using AI to propose further energy-saving measures. As a result, the Energy-Saving Operations Department can improve the energy efficiency of elevators and reduce operating costs.

[0097] The lifestyle rhythm learning unit can learn the lifestyle patterns of residents. For example, the lifestyle rhythm learning unit collects data such as residents' wake-up times, return times, and frequency of going out, and learns lifestyle patterns based on this data. The lifestyle rhythm learning unit can use AI to analyze residents' lifestyle patterns and provide information for setting the optimal waiting floor. Some or all of the above processing in the lifestyle rhythm learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the lifestyle rhythm learning unit can input residents' lifestyle pattern data into a generative AI, which can then analyze the lifestyle patterns and provide information for setting the optimal waiting floor. This makes it possible to optimize elevator operation based on residents' lifestyle patterns, improving convenience and efficiency.

[0098] The standby floor adjustment unit can set the optimal standby floor based on the lifestyle patterns learned by the lifestyle rhythm learning unit. The standby floor adjustment unit can shorten waiting times by, for example, having the elevator wait on a specific floor during times when residents frequently use it. The standby floor adjustment unit can dynamically adjust the standby floor based on the residents' lifestyle patterns using AI. Some or all of the above processing in the standby floor adjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the standby floor adjustment unit can input information provided by the lifestyle rhythm learning unit into the generative AI, which can then set the optimal standby floor. This optimizes the elevator's standby floor based on residents' lifestyle patterns, improving convenience and efficiency.

[0099] The visitor service department can provide priority service tailored to visitors. For example, the visitor service department can have the elevator wait on the first floor when a visitor arrives, ensuring a smooth arrival. The visitor service department can use AI to adjust priority service based on the importance and time of day of the visitor. Some or all of the above processing in the visitor service department may be performed using, for example, a generating AI, or without a generating AI. For example, the visitor service department can input visitor information into a generating AI, which can then adjust priority service. This improves convenience for residents by providing priority service tailored to visitors.

[0100] The cargo handling unit can adjust elevator operations during cargo delivery. For example, the cargo handling unit can adjust elevator operations during large-scale cargo deliveries to avoid affecting other residents. The cargo handling unit can use AI to adjust operations based on the type of cargo and delivery time. Some or all of the above-described processes in the cargo handling unit may be performed using, for example, a generating AI, or without a generating AI. For example, the cargo handling unit can input cargo information into a generating AI, which can then perform the operation adjustments. This allows for improved convenience for residents by adjusting operations during cargo delivery.

[0101] The security enhancement unit can perform access control using facial recognition. For example, the security enhancement unit can recognize residents' faces and restrict elevator use to prevent intruders from entering. The security enhancement unit can use AI to improve the accuracy of facial recognition and optimize the authentication process. Some or all of the above processing in the security enhancement unit may be performed using, for example, generative AI, or without generative AI. For example, the security enhancement unit can input residents' facial data into generative AI, which will perform facial recognition and implement access control. This strengthens security measures and improves the safety of residents by implementing access control using facial recognition.

[0102] The energy-saving operation unit can optimize energy efficiency. For example, the energy-saving operation unit saves energy by automatically turning off air conditioning and lighting during times when the elevator is not in use. The energy-saving operation unit can use AI to adjust methods for reducing power consumption and the operation schedule. Some or all of the above processes in the energy-saving operation unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the energy-saving operation unit can input elevator operation data into a generating AI, which can then adjust the operation schedule to optimize energy efficiency. This can optimize energy efficiency and reduce elevator operating costs.

[0103] The notification unit can notify users of waiting times and congestion levels in real time via a smartphone app. For example, residents can check the current waiting times and congestion levels on the app before using the elevator, allowing them to use the elevator efficiently. The notification unit can use AI to analyze waiting times and congestion levels in real time and notify residents. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input elevator operation data into a generative AI, which can analyze waiting times and congestion levels and notify residents in real time. This allows residents to understand elevator usage and use the elevator efficiently.

[0104] The lifestyle rhythm learning unit can estimate residents' emotions and improve the accuracy of learning lifestyle rhythms based on the estimated emotions. For example, if a resident is feeling stressed, the lifestyle rhythm learning unit can adjust the elevator waiting floor to provide a relaxing environment. If a resident is tired, the lifestyle rhythm learning unit can set the elevator waiting floor to a floor close to the resident's room to minimize travel. If a resident is in a hurry, the lifestyle rhythm learning unit can set the elevator waiting floor to the entrance to allow for quick movement. 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. Some or all of the above processing in the lifestyle rhythm learning unit may be performed using AI, for example, or without AI. For example, the lifestyle rhythm learning unit can input resident emotion data into a generative AI, which can estimate emotions and improve the accuracy of learning lifestyle rhythms. This allows for the learning of more accurate lifestyle patterns by improving the accuracy of learning lifestyle rhythms based on residents' emotions.

[0105] The lifestyle rhythm learning unit can analyze residents' past behavioral history and predict changes in their lifestyle rhythm. For example, the lifestyle rhythm learning unit can analyze the time periods that residents frequently used in the past and adjust the elevator waiting floors during those times. The lifestyle rhythm learning unit can adjust the elevator waiting floors during specific events or occasions based on residents' past behavioral history. The lifestyle rhythm learning unit can analyze residents' behavioral patterns and adjust the elevator waiting floors according to seasonal and weather changes. Some or all of the above processing in the lifestyle rhythm learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the lifestyle rhythm learning unit can input residents' behavioral history data into a generative AI, which can analyze the behavioral history and predict changes in their lifestyle rhythm. This allows for the prediction of changes in lifestyle rhythms and the optimization of elevator operation by analyzing residents' past behavioral history.

[0106] The lifestyle rhythm learning unit can dynamically adjust the elevator's waiting floor based on the residents' lifestyle rhythms. For example, if a resident frequently uses the elevator during the morning commute, the lifestyle rhythm learning unit can set the elevator's waiting floor to the first floor. If a resident returns home at night, the lifestyle rhythm learning unit can set the elevator's waiting floor to an upper floor. If a resident frequently uses the elevator on holidays, the lifestyle rhythm learning unit can adjust the elevator's waiting floor to coincide with that time. Some or all of the above processing in the lifestyle rhythm learning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the lifestyle rhythm learning unit can input residents' lifestyle rhythm data into a generating AI, which can then dynamically adjust the waiting floor. This allows for improved convenience by dynamically adjusting the elevator's waiting floor based on the residents' lifestyle rhythms.

[0107] The lifestyle rhythm learning unit can estimate the emotions of residents and adjust the frequency of learning lifestyle rhythms based on the estimated emotions. For example, if a resident is feeling stressed, the lifestyle rhythm learning unit can reduce the learning frequency to alleviate the resident's burden. If a resident is relaxed, the lifestyle rhythm learning unit can increase the learning frequency to gain a more detailed understanding of their lifestyle rhythm. If a resident is in a hurry, the lifestyle rhythm learning unit can temporarily stop the learning frequency to focus on elevator operation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the lifestyle rhythm learning unit may be performed using AI or not using AI. For example, the lifestyle rhythm learning unit can input resident emotion data into a generative AI, which can estimate the emotion and adjust the frequency of learning lifestyle rhythms. This reduces the burden on residents by adjusting the frequency of learning lifestyle rhythms based on their emotions.

[0108] The lifestyle rhythm learning unit can optimize the elevator operation schedule based on the residents' lifestyle rhythms. For example, the lifestyle rhythm learning unit can adjust the elevator operation schedule to match the residents' commuting times. The lifestyle rhythm learning unit can adjust the elevator operation schedule to match the residents' returning home times. The lifestyle rhythm learning unit can adjust the elevator operation schedule to match the residents' holiday activity patterns. Some or all of the above processing in the lifestyle rhythm learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the lifestyle rhythm learning unit can input residents' lifestyle rhythm data into a generative AI, which can then optimize the operation schedule. This improves convenience by optimizing the elevator operation schedule based on the residents' lifestyle rhythms.

[0109] The lifestyle rhythm learning unit can adjust the elevator maintenance schedule based on the residents' lifestyle rhythms. For example, the lifestyle rhythm learning unit can perform maintenance during times when residents are not using the elevator. The lifestyle rhythm learning unit can adjust the frequency of maintenance to match the residents' lifestyle rhythms. The lifestyle rhythm learning unit can analyze residents' behavior patterns and set an optimal maintenance schedule. Some or all of the above processes in the lifestyle rhythm learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the lifestyle rhythm learning unit can input residents' lifestyle rhythm data into a generative AI, which can then adjust the maintenance schedule. This improves convenience by adjusting the elevator maintenance schedule based on the residents' lifestyle rhythms.

[0110] The standby floor adjustment unit can estimate the emotions of residents and adjust the standby floor setting based on the estimated emotions. For example, if a resident is feeling stressed, the standby floor of the elevator can be set to a floor close to the resident's room. If a resident is relaxed, the standby floor can be set to the entrance. If a resident is in a hurry, the standby floor can be set to the first floor. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the standby floor adjustment unit may be performed using AI, for example, or without AI. For example, the standby floor adjustment unit can input resident emotion data into a generative AI, which can estimate the emotion and adjust the standby floor setting. This improves convenience by adjusting the standby floor setting based on the resident's emotions.

[0111] The standby floor adjustment unit can select the optimal standby floor by referring to the resident's past usage history when setting the standby floor. For example, the standby floor adjustment unit can set a floor that the resident has frequently used in the past as the standby floor. The standby floor adjustment unit can select the optimal standby floor for a specific time period based on the resident's past usage history. The standby floor adjustment unit can analyze the resident's behavior patterns and select the optimal standby floor. Some or all of the above processing in the standby floor adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the standby floor adjustment unit can input the resident's usage history data into a generating AI, which can analyze the usage history and select the optimal standby floor. This improves convenience by selecting the optimal standby floor by referring to the resident's past usage history.

[0112] The standby floor adjustment unit can dynamically change the standby floor when setting the standby floor, taking into account the current living situation of the residents. For example, if a resident is in a hurry, the standby floor adjustment unit can set the elevator standby floor to the first floor. If a resident is relaxed, the standby floor adjustment unit can set the elevator standby floor to the entrance. If a resident is carrying luggage, the standby floor adjustment unit can set the elevator standby floor to a floor close to the resident's room. Some or all of the above processing in the standby floor adjustment unit may be performed using, for example, a generating AI, or without a generating AI. For example, the standby floor adjustment unit can input resident living situation data into a generating AI, which can analyze the living situation and dynamically change the standby floor. This improves convenience by dynamically changing the standby floor considering the current living situation of the residents.

[0113] The standby floor adjustment unit can estimate the emotions of residents and determine the priority of standby floors based on the estimated emotions. For example, if a resident is feeling stressed, the standby floor of the elevator can be set to a floor close to the resident's room. If a resident is relaxed, the standby floor of the elevator can be set to the entrance. If a resident is in a hurry, the standby floor of the elevator can be set to the first floor. 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. Some or all of the above processing in the standby floor adjustment unit may be performed using AI or not using AI. For example, the standby floor adjustment unit can input resident emotion data into a generative AI, which can estimate the emotion and determine the priority of standby floors. This improves convenience by determining the priority of standby floors based on the emotions of residents.

[0114] The standby floor adjustment unit can select the optimal standby floor when setting the standby floor, taking into account the resident's geographical location information. For example, if the resident is at their current location, the standby floor adjustment unit can set the elevator standby floor to a floor close to the resident's room. If the resident is in the entrance, the standby floor adjustment unit can set the elevator standby floor to the first floor. If the resident is on a common floor, the standby floor adjustment unit can set the elevator standby floor to the common floor. Some or all of the above processing in the standby floor adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the standby floor adjustment unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal standby floor. This improves convenience by selecting the optimal standby floor considering the resident's geographical location information.

[0115] The standby floor adjustment unit can adjust the standby floor by analyzing residents' social media activity when setting the standby floor. For example, if a resident posts their current location on social media, the standby floor of the elevator can be set to that location. If a resident participates in an event on social media, the standby floor of the elevator can be set to the event venue. If a resident indicates on social media that they are in a specific location, the standby floor of the elevator can be set to that location. Some or all of the above processing in the standby floor adjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the standby floor adjustment unit can input resident social media activity data into a generative AI, which can analyze the social media activity and adjust the standby floor. This improves convenience by analyzing residents' social media activity and adjusting the standby floor.

[0116] The visitor reception unit can estimate the emotions of residents and adjust its visitor reception methods based on the estimated emotions. For example, if a resident is feeling stressed, the visitor reception unit can respond quickly to reduce the resident's burden. If a resident is relaxed, the visitor reception unit can respond carefully to improve the resident's satisfaction. If a resident is in a hurry, the visitor reception unit can respond quickly to save the resident's time. 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. Some or all of the above processing in the visitor reception unit may be performed using AI, for example, or not using AI. For example, the visitor reception unit can input resident emotion data into a generative AI, which can estimate the emotion and adjust its visitor reception methods. This improves convenience by adjusting visitor reception methods based on the resident's emotions.

[0117] The visitor response unit can select the optimal response method by referring to the resident's past visitor history when responding to visitors. For example, the visitor response unit can adjust the elevator waiting floor to match the time of day when the resident frequently received visitors in the past. The visitor response unit can adjust the elevator waiting floor for specific events or occasions based on the resident's past visitor history. The visitor response unit can analyze the resident's visitor patterns and select the optimal response method. Some or all of the above processes in the visitor response unit may be performed using, for example, a generating AI, or without a generating AI. For example, the visitor response unit can input the resident's visitor history data into a generating AI, which can analyze the visitor history and select the optimal response method. This improves convenience by selecting the optimal response method by referring to the resident's past visitor history.

[0118] The visitor reception department can customize its response methods when receiving visitors, taking into account the resident's current living situation. For example, if a resident is in a hurry, the visitor reception department can respond quickly, saving the resident's time. If a resident is relaxed, the visitor reception department can respond carefully, improving the resident's satisfaction. If a resident is carrying luggage, the visitor reception department can respond quickly, reducing the resident's burden. Some or all of the above processes in the visitor reception department may be performed using, for example, a generative AI, or not using a generative AI. For example, the visitor reception department can input data on the resident's living situation into a generative AI, which can then analyze the situation and customize the response method. This improves convenience by customizing the response method to take into account the resident's current living situation.

[0119] The visitor reception department can estimate the emotions of residents and determine the priority of visitor service based on the estimated emotions. For example, if a resident is feeling stressed, the visitor reception department can provide prompt service to reduce the resident's burden. If a resident is relaxed, the visitor reception department can provide attentive service to improve resident satisfaction. If a resident is in a hurry, the visitor reception department can provide prompt service to save the resident's time. 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. Some or all of the above processing in the visitor reception department may be performed using AI, or not using AI. For example, the visitor reception department can input resident emotion data into a generative AI, which can estimate the emotion and determine the priority of visitor service. This improves convenience by determining the priority of visitor service based on the resident's emotions.

[0120] The visitor reception department can select the optimal response method when receiving visitors, taking into account the resident's geographical location information. For example, if the resident is at their current location, the visitor reception department can respond quickly, reducing the resident's burden. If the resident is at the entrance, the visitor reception department can respond quickly, saving the resident's time. If the resident is on a common floor, the visitor reception department can respond quickly, improving resident satisfaction. Some or all of the above processing in the visitor reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the visitor reception department can input the resident's geographical location information into a generative AI, which can analyze the geographical location information and select the optimal response method. This improves convenience by selecting the optimal response method considering the resident's geographical location information.

[0121] The visitor service department can analyze residents' social media activity and adjust its response methods when handling visitors. For example, if a resident posts about an upcoming visit on social media, the visitor service department can adjust the elevator's waiting floor to coincide with that time. If a resident is participating in an event on social media, the visitor service department can provide prompt assistance, reducing the burden on the resident. If a resident indicates on social media that they are in a specific location, the visitor service department can provide prompt assistance, improving resident satisfaction. Some or all of the above processes in the visitor service department may be performed using, for example, a generative AI, or not. For example, the visitor service department can input resident social media activity data into a generative AI, which can then analyze the social media activity and adjust its response methods. This allows for improved convenience by analyzing residents' social media activity and adjusting response methods accordingly.

[0122] The package handling unit can estimate the emotions of residents and adjust the package handling method based on the estimated emotions. For example, if a resident is stressed, the package handling unit can handle the package quickly to reduce the resident's burden. If a resident is relaxed, the package handling unit can handle the package carefully to improve the resident's satisfaction. If a resident is in a hurry, the package handling unit can handle the package quickly to save the resident's time. 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. Some or all of the above processing in the package handling unit may be performed using AI or not. For example, the package handling unit can input resident emotion data into a generative AI, which can estimate the emotion and adjust the package handling method. This improves convenience by adjusting the package handling method based on the resident's emotions.

[0123] The cargo handling unit can select the optimal handling method by referring to the resident's past cargo delivery history when handling cargo. For example, the cargo handling unit can adjust the elevator waiting floor to match the time of day when the resident frequently delivered cargo in the past. The cargo handling unit can adjust the elevator waiting floor during specific events or occasions based on the resident's past cargo delivery history. The cargo handling unit can analyze the resident's cargo delivery patterns and select the optimal handling method. Some or all of the above processing in the cargo handling unit may be performed using, for example, a generating AI, or without a generating AI. For example, the cargo handling unit can input the resident's cargo delivery history data into a generating AI, which can analyze the cargo delivery history and select the optimal handling method. This improves convenience by selecting the optimal handling method by referring to the resident's past cargo delivery history.

[0124] The package handling unit can customize its handling methods when handling packages, taking into account the resident's current living situation. For example, if a resident is in a hurry, the package handling unit can handle the package quickly, saving the resident time. If a resident is relaxed, the package handling unit can handle the package carefully, improving the resident's satisfaction. If a resident is carrying a package, the package handling unit can handle the package quickly, reducing the resident's burden. Some or all of the above processes in the package handling unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the package handling unit can input resident living situation data into a generative AI, which can analyze the living situation and customize the handling method. This can improve convenience by customizing the handling method to take into account the resident's current living situation.

[0125] The package handling unit can estimate residents' emotions and determine the priority of package handling based on the estimated emotions. For example, if a resident is stressed, the package handling unit can handle the package quickly to reduce the resident's burden. If a resident is relaxed, the package handling unit can handle the package carefully to improve the resident's satisfaction. If a resident is in a hurry, the package handling unit can handle the package quickly to save the resident's time. 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. Some or all of the above processing in the package handling unit may be performed using AI, for example, or not using AI. For example, the package handling unit can input resident emotion data into a generative AI, which can estimate emotions and determine the priority of package handling. This improves convenience by determining the priority of package handling based on residents' emotions.

[0126] The package handling unit can select the optimal handling method when handling packages, taking into account the resident's geographical location information. For example, if the resident is at their current location, the package handling unit can handle the package quickly, reducing the resident's burden. If the resident is at the entrance, the package handling unit can handle the package quickly, saving the resident's time. If the resident is on a common floor, the package handling unit can handle the package quickly, improving resident satisfaction. Some or all of the above processing in the package handling unit may be performed using, for example, a generating AI, or without a generating AI. For example, the package handling unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal handling method. This improves convenience by selecting the optimal handling method considering the resident's geographical location information.

[0127] The package handling unit can analyze residents' social media activity and adjust its handling methods when handling packages. For example, if a resident posts on social media about the expected arrival date of a package, the package handling unit can adjust the elevator waiting floor to that time. If a resident is participating in an event on social media, the package handling unit can expedite the package handling to reduce the resident's burden. If a resident indicates on social media that they are in a specific location, the package handling unit can expedite the package handling to improve resident satisfaction. Some or all of the above processing in the package handling unit may be performed using, for example, generative AI, or not using generative AI. For example, the package handling unit can input resident social media activity data into generative AI, which can analyze the social media activity and adjust the handling method. This can improve convenience by analyzing residents' social media activity and adjusting the handling method accordingly.

[0128] The Security Enhancement Department can estimate residents' emotions and adjust security enhancement methods based on the estimated emotions. For example, if residents are feeling stressed, the Security Enhancement Department can quickly implement security enhancements to increase their sense of security. If residents are relaxed, the Security Enhancement Department can carefully implement security enhancements to improve their satisfaction. If residents are in a hurry, the Security Enhancement Department can quickly implement security enhancements to save them time. 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. Some or all of the above processing in the Security Enhancement Department may be performed using AI or not. For example, the Security Enhancement Department can input residents' emotion data into a generative AI, which can estimate emotions and adjust security enhancement methods. This allows for an improvement in sense of security by adjusting security enhancement methods based on residents' emotions.

[0129] The Crime Prevention Enhancement Department can select the most appropriate crime prevention method by referring to residents' past crime prevention history when enhancing crime prevention measures. For example, the Crime Prevention Enhancement Department can adjust crime prevention measures to match the time periods in which residents have frequently taken extra precautions in the past. The Crime Prevention Enhancement Department can adjust crime prevention measures for specific events or occasions based on residents' past crime prevention history. The Crime Prevention Enhancement Department can analyze residents' crime prevention patterns and select the most appropriate crime prevention method. Some or all of the above processes in the Crime Prevention Enhancement Department may be performed using, for example, a generating AI, or without a generating AI. For example, the Crime Prevention Enhancement Department can input residents' crime prevention history data into a generating AI, which can then analyze the history and select the most appropriate crime prevention method. This allows for an improvement in residents' sense of security by selecting the most appropriate crime prevention method based on their past crime prevention history.

[0130] The crime prevention enhancement department can customize crime prevention methods when enhancing crime prevention, taking into account the current living situation of residents. For example, if a resident is in a hurry, the crime prevention enhancement department can quickly enhance crime prevention to save the resident's time. If a resident is relaxed, the crime prevention enhancement department can carefully enhance crime prevention to improve the resident's satisfaction. If a resident is carrying luggage, the crime prevention enhancement department can quickly enhance crime prevention to reduce the resident's burden. Some or all of the above processes in the crime prevention enhancement department may be performed using, for example, a generating AI, or not using a generating AI. For example, the crime prevention enhancement department can input data on the resident's living situation into a generating AI, which can analyze the living situation and customize crime prevention methods. This can improve the sense of security by customizing crime prevention methods to take into account the resident's current living situation.

[0131] The Security Enhancement Department can estimate residents' emotions and determine the priority of security enhancements based on the estimated emotions. For example, if residents are feeling stressed, the Security Enhancement Department can quickly implement security enhancements to increase their sense of security. If residents are relaxed, the Security Enhancement Department can carefully implement security enhancements to improve their satisfaction. If residents are in a hurry, the Security Enhancement Department can quickly implement security enhancements to save them time. 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. Some or all of the above processing in the Security Enhancement Department may be performed using AI or not. For example, the Security Enhancement Department can input residents' emotion data into a generative AI, which can estimate emotions and determine the priority of security enhancements. This allows for an improvement in sense of security by determining the priority of security enhancements based on residents' emotions.

[0132] The Security Enhancement Department can select the optimal security method when enhancing security, taking into account the geographical location information of residents. For example, if a resident is at their current location, the Security Enhancement Department can quickly implement security enhancements to increase the resident's sense of security. If a resident is at the entrance, the Security Enhancement Department can quickly implement security enhancements to save the resident's time. If a resident is on a common floor, the Security Enhancement Department can quickly implement security enhancements to improve resident satisfaction. Some or all of the above processing in the Security Enhancement Department may be performed using, for example, a generating AI, or without using a generating AI. For example, the Security Enhancement Department can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal security method. This allows for the selection of the optimal security method while considering the resident's geographical location information, thereby improving their sense of security.

[0133] The Crime Prevention Enhancement Department can analyze residents' social media activity and adjust crime prevention methods when enhancing crime prevention. For example, if a resident posts on social media about the need for crime prevention, the Crime Prevention Enhancement Department can quickly implement enhanced crime prevention measures to increase residents' sense of security. If a resident participates in an event on social media, the Crime Prevention Enhancement Department can quickly implement enhanced crime prevention measures to reduce the burden on residents. If a resident indicates on social media that they are in a specific location, the Crime Prevention Enhancement Department can quickly implement enhanced crime prevention measures to improve residents' satisfaction. Some or all of the above processes in the Crime Prevention Enhancement Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Crime Prevention Enhancement Department can input residents' social media activity data into a generative AI, which can analyze the social media activity and adjust crime prevention methods. This allows for an improvement in residents' sense of security by analyzing their social media activity and adjusting crime prevention methods accordingly.

[0134] The energy-saving operation unit can estimate residents' emotions and adjust energy-saving operation methods based on the estimated emotions. For example, if residents are stressed, the energy-saving operation unit can quickly implement energy-saving operation to reduce their burden. If residents are relaxed, the energy-saving operation unit can carefully implement energy-saving operation to improve their satisfaction. If residents are in a hurry, the energy-saving operation unit can quickly implement energy-saving operation to save them time. 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. Some or all of the above processing in the energy-saving operation unit may be performed using AI, for example, or without AI. For example, the energy-saving operation unit can input residents' emotion data into a generative AI, which can estimate emotions and adjust energy-saving operation methods. This allows for improved energy efficiency by adjusting energy-saving operation methods based on residents' emotions.

[0135] The energy-saving operation unit can select the optimal energy-saving method by referring to residents' past energy consumption history during energy-saving operation. For example, the energy-saving operation unit can adjust energy-saving operation to match the time periods when residents frequently consumed energy in the past. The energy-saving operation unit can adjust energy-saving operation during specific events or occasions based on residents' past energy consumption history. The energy-saving operation unit can analyze residents' energy consumption patterns and select the optimal energy-saving method. Some or all of the above processes in the energy-saving operation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the energy-saving operation unit can input residents' energy consumption history data into a generating AI, which can then analyze the energy consumption history and select the optimal energy-saving method. This allows for improved energy efficiency by selecting the optimal energy-saving method by referring to residents' past energy consumption history.

[0136] The energy-saving operation unit can customize energy-saving methods during energy-saving operation, taking into account the current living situation of residents. For example, if residents are in a hurry, the energy-saving operation unit can perform energy-saving operations quickly to save residents time. If residents are relaxed, the energy-saving operation unit can perform energy-saving operations carefully to improve residents' satisfaction. If residents are carrying luggage, the energy-saving operation unit can perform energy-saving operations quickly to reduce the burden on residents. Some or all of the above processes in the energy-saving operation unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the energy-saving operation unit can input residents' living situation data into a generating AI, which can analyze the living situation and customize energy-saving methods. This allows for improved energy efficiency by customizing energy-saving methods to take into account the current living situation of residents.

[0137] The energy-saving operation unit can estimate residents' emotions and determine energy-saving operation priorities based on those estimated emotions. For example, if residents are feeling stressed, the energy-saving operation unit can quickly implement energy-saving operations to reduce their burden. If residents are relaxed, the energy-saving operation unit can carefully implement energy-saving operations to improve their satisfaction. If residents are in a hurry, the energy-saving operation unit can quickly implement energy-saving operations to save them time. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above processing in the energy-saving operation unit may be performed using AI or not. For example, the energy-saving operation unit can input residents' emotion data into a generative AI, which can estimate emotions and determine energy-saving operation priorities. This allows for improved energy efficiency by determining energy-saving operation priorities based on residents' emotions.

[0138] The energy-saving operation unit can select the optimal energy-saving method by considering the geographical location information of residents during energy-saving operation. For example, if a resident is at their current location, the energy-saving operation unit can quickly implement energy-saving operation to reduce the burden on the resident. If a resident is in the entrance, the energy-saving operation unit can quickly implement energy-saving operation to save the resident's time. If a resident is on a common floor, the energy-saving operation unit can quickly implement energy-saving operation to improve resident satisfaction. Some or all of the above processing in the energy-saving operation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the energy-saving operation unit can input the geographical location information of residents into a generating AI, which can analyze the geographical location information and select the optimal energy-saving method. This allows for improved energy efficiency by selecting the optimal energy-saving method by considering the geographical location information of residents.

[0139] The Energy-Saving Operations Department can analyze residents' social media activity during energy-saving operations and adjust energy-saving methods accordingly. For example, if a resident posts about the need for energy saving on social media, the Energy-Saving Operations Department can quickly implement energy-saving operations to reduce the burden on the resident. If a resident participates in an event on social media, the Energy-Saving Operations Department can quickly implement energy-saving operations to reduce the burden on the resident. If a resident indicates on social media that they are in a specific location, the Energy-Saving Operations Department can quickly implement energy-saving operations to improve resident satisfaction. Some or all of the above processing in the Energy-Saving Operations Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Energy-Saving Operations Department can input resident social media activity data into a generative AI, which can then analyze the social media activity and adjust energy-saving methods. This allows for improved energy efficiency by analyzing residents' social media activity and adjusting energy-saving methods accordingly.

[0140] The assistance unit can estimate the emotions of residents and adjust the method of providing assistance functions based on the estimated emotions. For example, if a resident is feeling stressed, the assistance unit can quickly provide assistance functions to reduce the resident's burden. If a resident is relaxed, the assistance unit can carefully provide assistance functions to improve the resident's satisfaction. If a resident is in a hurry, the assistance unit can quickly provide assistance functions to save the resident's time. 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. Some or all of the above processing in the assistance unit may be performed using AI, for example, or not using AI. For example, the assistance unit can input resident emotion data into a generative AI, which can estimate the emotion and adjust the method of providing assistance functions. This improves convenience by adjusting the method of providing assistance functions based on the resident's emotions.

[0141] The assistance unit can select the optimal assistance method by referring to the resident's past usage history when providing assistance functions. For example, the assistance unit can prioritize providing assistance functions that the resident has frequently used in the past. The assistance unit can select the optimal assistance method for a specific time period based on the resident's past usage history. The assistance unit can analyze the resident's usage patterns and select the optimal assistance method. Some or all of the above processing in the assistance unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the assistance unit can input the resident's usage history data into a generating AI, which can then analyze the usage history and select the optimal assistance method. This improves convenience by selecting the optimal assistance method by referring to the resident's past usage history.

[0142] The assistance unit can estimate the emotions of residents and determine the priority of assistance functions based on the estimated emotions. For example, if a resident is feeling stressed, the assistance unit can quickly provide assistance functions to reduce the resident's burden. If a resident is relaxed, the assistance unit can carefully provide assistance functions to improve the resident's satisfaction. If a resident is in a hurry, the assistance unit can quickly provide assistance functions to save the resident's time. 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. Some or all of the above processing in the assistance unit may be performed using AI, for example, or not using AI. For example, the assistance unit can input resident emotion data into a generative AI, which can estimate emotions and determine the priority of assistance functions. This improves convenience by determining the priority of assistance functions based on the resident's emotions.

[0143] The assistance unit can select the optimal assistance method by considering the resident's geographical location information when providing assistance functions. For example, if the resident is at their current location, the assistance unit can quickly provide assistance functions to reduce the resident's burden. If the resident is at the entrance, the assistance unit can quickly provide assistance functions to save the resident's time. If the resident is on a shared floor, the assistance unit can quickly provide assistance functions to improve resident satisfaction. Some or all of the above processing in the assistance unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the assistance unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal assistance method. This improves convenience by selecting the optimal assistance method by considering the resident's geographical location information.

[0144] The settings unit can estimate the residents' emotions and adjust the settings method based on the estimated emotions. For example, if a resident is stressed, the settings unit can perform the settings quickly to reduce the resident's burden. If a resident is relaxed, the settings unit can perform the settings carefully to improve the resident's satisfaction. If a resident is in a hurry, the settings unit can perform the settings quickly to save the resident's time. 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. Some or all of the above processing in the settings unit may be performed using AI, or not using AI. For example, the settings unit can input resident emotion data into the generative AI, which can estimate the emotions and adjust the settings method. This improves convenience by adjusting the settings method based on the resident's emotions.

[0145] The settings unit can select the optimal settings method by referring to the resident's past settings history during the settings process. For example, the settings unit may prioritize providing settings that the resident has frequently used in the past. The settings unit can select the optimal settings method for a specific time period based on the resident's past settings history. The settings unit can analyze the resident's settings patterns and select the optimal settings method. Some or all of the above-described processes in the settings unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the settings unit can input the resident's settings history data into a generating AI, which can then analyze the settings history and select the optimal settings method. This improves usability by selecting the optimal settings method by referring to the resident's past settings history.

[0146] The settings unit can estimate the residents' emotions and determine the priority of settings based on the estimated emotions. For example, if a resident is stressed, the settings unit can perform the settings quickly to reduce the resident's burden. If a resident is relaxed, the settings unit can perform the settings carefully to improve the resident's satisfaction. If a resident is in a hurry, the settings unit can perform the settings quickly to save the resident's time. 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. Some or all of the above processing in the settings unit may be performed using AI or not using AI. For example, the settings unit can input resident emotion data into the generative AI, which can estimate the emotions and determine the priority of settings. This improves convenience by determining the priority of settings based on the resident's emotions.

[0147] The configuration unit can select the optimal configuration method by considering the resident's geographical location information during configuration. For example, if the resident is at their current location, the configuration unit can quickly configure the system, reducing the burden on the resident. If the resident is at the entrance, the configuration unit can quickly configure the system, saving the resident time. If the resident is on a shared floor, the configuration unit can quickly configure the system, improving resident satisfaction. Some or all of the above-described processes in the configuration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the configuration unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal configuration method. This improves convenience by selecting the optimal configuration method by considering the resident's geographical location information.

[0148] The operations planning department can estimate residents' emotions and adjust the operation planning method based on the estimated emotions. For example, if residents are stressed, the operations planning department can expedite the operation planning to reduce their burden. If residents are relaxed, the operations planning department can expedite the operation planning to improve their satisfaction. If residents are in a hurry, the operations planning department can expedite the operation planning to save them time. 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. Some or all of the above processing in the operations planning department may be performed using AI or not. For example, the operations planning department can input residents' emotion data into a generative AI, which can estimate emotions and adjust the operation planning method. This improves convenience by adjusting the operation planning method based on residents' emotions.

[0149] The transportation planning department can select the optimal transportation plan by referring to residents' past transportation history when planning transportation. For example, the transportation planning department can adjust the transportation plan to match the time slots that residents have frequently used in the past. The transportation planning department can adjust the transportation plan for specific events or occasions based on residents' past transportation history. The transportation planning department can analyze residents' transportation patterns and select the optimal transportation plan. Some or all of the above processes in the transportation planning department may be performed using, for example, a generating AI, or not using a generating AI. For example, the transportation planning department can input residents' transportation history data into a generating AI, which can analyze the transportation history and select the optimal transportation plan. This improves convenience by selecting the optimal transportation plan by referring to residents' past transportation history.

[0150] The operations planning department can estimate residents' emotions and determine the priority of operations based on the estimated emotions. For example, if residents are stressed, the operations planning department can expedite operations to reduce their burden. If residents are relaxed, the operations planning department can expedite operations to improve their satisfaction. If residents are in a hurry, the operations planning department can expedite operations to save them time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the operations planning department may be performed using AI or not. For example, the operations planning department can input residents' emotion data into a generative AI, which can estimate emotions and determine the priority of operations. This improves convenience by determining the priority of operations based on residents' emotions.

[0151] The transportation planning department can select the optimal transportation plan when planning transportation, taking into account the geographical location information of residents. For example, if a resident is at their current location, the transportation planning department can quickly create a transportation plan, reducing the burden on the resident. If a resident is at the entrance, the transportation planning department can quickly create a transportation plan, saving the resident time. If a resident is on a common floor, the transportation planning department can quickly create a transportation plan, improving resident satisfaction. Some or all of the above processing in the transportation planning department may be performed using, for example, a generating AI, or without a generating AI. For example, the transportation planning department can input the geographical location information of residents into a generating AI, which can analyze the geographical location information and select the optimal transportation plan. This improves convenience by selecting the optimal transportation plan while taking into account the geographical location information of residents.

[0152] The security mode unit can estimate the emotions of residents and adjust the security mode method based on the estimated emotions. For example, if a resident is feeling stressed, the security mode unit can activate the security mode quickly to increase the resident's sense of security. If a resident is relaxed, the security mode unit can activate the security mode carefully to improve the resident's satisfaction. If a resident is in a hurry, the security mode unit can activate the security mode quickly to save the resident's time. 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. Some or all of the above processing in the security mode unit may be performed using AI or not using AI. For example, the security mode unit can input resident emotion data into the generative AI, which can estimate the emotion and adjust the security mode method. This allows for an improvement in the sense of security by adjusting the security mode method based on the resident's emotions.

[0153] The security mode unit can select the optimal security method by referring to the resident's past security history when security mode is activated. For example, the security mode unit can adjust the security mode to match the time period in which the resident frequently activated security mode in the past. The security mode unit can adjust the security mode for specific events or occasions based on the resident's past security history. The security mode unit can analyze the resident's security patterns and select the optimal security method. Some or all of the above processing in the security mode unit may be performed using, for example, a generating AI, or without a generating AI. For example, the security mode unit can input the resident's security history data into a generating AI, which can analyze the security history and select the optimal security method. This can improve the sense of security by selecting the optimal security method by referring to the resident's past security history.

[0154] The security mode unit can customize security methods when implementing security mode, taking into account the current living situation of the residents. For example, if a resident is in a hurry, the security mode unit can quickly implement security mode to save the resident's time. If a resident is relaxed, the security mode unit can carefully implement security mode to improve resident satisfaction. If a resident is carrying luggage, the security mode unit can quickly implement security mode to reduce the resident's burden. Some or all of the above processing in the security mode unit may be performed using, for example, a generating AI, or without a generating AI. For example, the security mode unit can input resident living situation data into a generating AI, which can analyze the living situation and customize security methods. This can improve the sense of security by customizing security methods to take into account the current living situation of the residents.

[0155] The security mode unit can estimate the emotions of residents and determine the priority of security modes based on the estimated emotions. For example, if a resident is feeling stressed, the security mode unit can quickly activate security modes to increase the resident's sense of security. If a resident is relaxed, the security mode unit can carefully activate security modes to improve the resident's satisfaction. If a resident is in a hurry, the security mode unit can quickly activate security modes to save the resident's time. 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. Some or all of the above processing in the security mode unit may be performed using AI or not using AI. For example, the security mode unit can input resident emotion data into a generative AI, which can estimate emotions and determine the priority of security modes. This improves the sense of security by determining the priority of security modes based on the resident's emotions.

[0156] The security mode unit can select the optimal security method by considering the geographical location information of residents when implementing security mode. For example, if a resident is at their current location, the security mode unit can quickly activate security mode to enhance the resident's sense of security. If a resident is at the entrance, the security mode unit can quickly activate security mode to save the resident's time. If a resident is on a shared floor, the security mode unit can quickly activate security mode to improve resident satisfaction. Some or all of the above processing in the security mode unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the security mode unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal security method. This allows for an improvement in the resident's sense of security by selecting the optimal security method by considering their geographical location information.

[0157] The security mode unit can analyze residents' social media activity and adjust security methods when security mode is implemented. For example, if a resident posts on social media that they need security, the security mode unit can quickly activate security mode to increase the resident's sense of security. If a resident participates in an event on social media, the security mode unit can quickly activate security mode to reduce the resident's burden. If a resident indicates on social media that they are in a specific location, the security mode unit can quickly activate security mode to improve resident satisfaction. Some or all of the above processing in the security mode unit may be performed using, for example, a generative AI, or without a generative AI. For example, the security mode unit can input resident social media activity data into a generative AI, which can analyze the social media activity and adjust security methods. This allows for an improvement in the resident's sense of security by analyzing their social media activity and adjusting security methods accordingly.

[0158] The adjustment unit can estimate the residents' emotions and adjust the adjustment method based on the estimated emotions. For example, if a resident is stressed, the adjustment unit can quickly adjust to reduce the resident's burden. If a resident is relaxed, the adjustment unit can carefully adjust to improve the resident's satisfaction. If a resident is in a hurry, the adjustment unit can quickly adjust to save the resident's time. 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. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input resident emotion data into a generative AI, which can estimate the emotion and adjust the adjustment method. This improves convenience by adjusting the adjustment method based on the resident's emotions.

[0159] The adjustment unit can select the optimal adjustment method by referring to the resident's past adjustment history during the adjustment process. For example, the adjustment unit may prioritize providing adjustments that the resident has frequently performed in the past. The adjustment unit can select the optimal adjustment method for a specific time period based on the resident's past adjustment history. The adjustment unit can analyze the resident's adjustment patterns and select the optimal adjustment method. Some or all of the above processes in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit can input the resident's adjustment history data into a generating AI, which can then analyze the adjustment history and select the optimal adjustment method. This improves convenience by selecting the optimal adjustment method by referring to the resident's past adjustment history.

[0160] The adjustment unit can estimate residents' emotions and determine adjustment priorities based on the estimated emotions. For example, if a resident is stressed, the adjustment unit can perform adjustments quickly to reduce the resident's burden. If a resident is relaxed, the adjustment unit can perform adjustments carefully to improve the resident's satisfaction. If a resident is in a hurry, the adjustment unit can perform adjustments quickly to save the resident's time. 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. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input resident emotion data into a generative AI, which can estimate emotions and determine adjustment priorities. This improves convenience by determining adjustment priorities based on residents' emotions.

[0161] The adjustment unit can select the optimal adjustment method by considering the geographical location information of residents during the adjustment process. For example, if a resident is at their current location, the adjustment unit can perform the adjustment quickly, reducing the burden on the resident. If a resident is at the entrance, the adjustment unit can perform the adjustment quickly, saving the resident time. If a resident is on a common floor, the adjustment unit can perform the adjustment quickly, improving resident satisfaction. Some or all of the above-described processes in the adjustment unit may be performed using, for example, a generating AI, or without a generating AI. For example, the adjustment unit can input the resident's geographical location information into a generating AI, which can analyze the geographical location information and select the optimal adjustment method. This improves convenience by selecting the optimal adjustment method by considering the resident's geographical location information.

[0162] The notification unit can estimate the emotions of residents and adjust the notification method based on the estimated emotions. For example, if a resident is feeling stressed, the notification unit can send a notification quickly to reduce the resident's burden. If a resident is relaxed, the notification unit can send a notification carefully to improve the resident's satisfaction. If a resident is in a hurry, the notification unit can send a notification quickly to save the resident's time. 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. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input resident emotion data into a generative AI, which can estimate the emotion and adjust the notification method. This can improve convenience by adjusting the notification method based on the resident's emotions.

[0163] The notification unit can select the optimal notification method by referring to the resident's past notification history when sending a notification. For example, the notification unit can prioritize providing notifications that the resident has frequently received in the past. The notification unit can select the optimal notification method for a specific time period based on the resident's past notification history. The notification unit can analyze the resident's notification patterns and select the optimal notification method. Some or all of the above processing in the notification unit may be performed using, for example, a generation AI, or without a generation AI. For example, the notification unit can input the resident's notification history data into a generation AI, which can then analyze the notification history and select the optimal notification method. This improves convenience by selecting the optimal notification method by referring to the resident's past notification history.

[0164] The notification unit can estimate residents' emotions and determine notification priorities based on the estimated emotions. For example, if a resident is stressed, the notification unit can send a notification quickly to reduce the resident's burden. If a resident is relaxed, the notification unit can send a notification carefully to improve the resident's satisfaction. If a resident is in a hurry, the notification unit can send a notification quickly to save the resident's time. 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. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input resident emotion data into a generative AI, which can estimate emotions and determine notification priorities. This improves convenience by determining notification priorities based on residents' emotions.

[0165] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0166] The smart elevator management AI agent can monitor residents' health status and operate elevators accordingly. For example, if a resident is feeling unwell, the elevator waiting floor can be set to a floor close to the resident's room to minimize travel. If a resident is not getting enough exercise, the elevator waiting floor can be set to a floor slightly further away to encourage walking. Furthermore, if a resident is injured, the elevator waiting floor can be set to the floor closest to the resident's room to support quick relocation. In this way, by operating elevators according to residents' health status, the system can support their well-being.

[0167] The smart elevator management AI agent can customize the elevator environment based on the resident's hobbies and preferences. For example, if a resident likes music, the elevator can play their favorite music. If a resident likes a particular scent, that scent can be diffused inside the elevator. Furthermore, if a resident prefers a specific type of lighting, the elevator's lighting can be adjusted to suit that resident's preference. In this way, by customizing the elevator environment based on the resident's hobbies and preferences, a more comfortable travel experience can be provided.

[0168] The smart elevator management AI agent can estimate the emotions of residents and adjust the elevator environment based on those estimates. For example, if a resident is feeling stressed, it can play relaxing music in the elevator. If a resident is tired, it can soften the lighting in the elevator to provide a relaxing environment. Furthermore, if a resident is in a hurry, it can increase the elevator's speed to support quick travel. In this way, by adjusting the elevator environment based on the resident's emotions, it can provide a comfortable travel experience.

[0169] The smart elevator management AI agent can analyze residents' past elevator usage history and optimize elevator operation based on that history. For example, it can reduce waiting times by having elevators wait during times when residents frequently used them in the past. It can also support quicker travel by having elevators wait on floors that residents have used in the past. Furthermore, it can analyze specific patterns from residents' usage history and adjust elevator operation based on those patterns. In this way, convenience can be improved by optimizing elevator operation based on residents' past usage history.

[0170] The smart elevator management AI agent can estimate residents' emotions and adjust elevator schedules based on those emotions. For example, if residents are stressed, the elevator schedule can be quickly adjusted to reduce waiting times. If residents are relaxed, the schedule can be adjusted slowly to provide a comfortable ride. Furthermore, if residents are in a hurry, the schedule can be quickly adjusted to support faster travel. In this way, convenience can be improved by adjusting elevator schedules based on residents' emotions.

[0171] The smart elevator management AI agent can optimize elevator operation using residents' geographical location information. For example, if a resident is currently at a location, the elevator can be made to wait at that location to support quick movement. Similarly, if a resident is at the entrance, the elevator can be made to wait on the first floor to support quick movement. Furthermore, if a resident is on a common floor, the elevator can be made to wait on that floor to support quick movement. In this way, by optimizing elevator operation using residents' geographical location information, convenience can be improved.

[0172] The smart elevator management AI agent can estimate residents' emotions and adjust the elevator's waiting floor based on those emotions. For example, if a resident is feeling stressed, the waiting floor can be set to a floor closer to their room to support quick travel. If a resident is relaxed, the waiting floor can be set to the entrance to provide a comfortable ride. Furthermore, if a resident is in a hurry, the waiting floor can be set to the first floor to support quick travel. In this way, convenience can be improved by adjusting the elevator's waiting floor based on residents' emotions.

[0173] The smart elevator management AI agent can analyze residents' social media activity and optimize elevator operations. For example, if a resident posts their current location on social media, the elevator can be made to wait at that location to support quick travel. Similarly, if a resident participates in an event on social media, the elevator can be made to wait at the event venue to support quick travel. Furthermore, if a resident indicates on social media that they are in a specific location, the elevator can be made to wait at that location to support quick travel. In this way, by analyzing residents' social media activity and optimizing elevator operations, convenience can be improved.

[0174] The smart elevator management AI agent can estimate residents' emotions and adjust the elevator's operating speed based on those emotions. For example, if a resident is stressed, the elevator's speed can be increased to support quick travel. Conversely, if a resident is relaxed, the elevator's speed can be slowed down to provide a comfortable ride. Furthermore, if a resident is in a hurry, the elevator's speed can be increased to support quick travel. This improves convenience by adjusting the elevator's operating speed based on residents' emotions.

[0175] The smart elevator management AI agent can analyze residents' past elevator usage history and optimize elevator operation based on that history. For example, it can reduce waiting times by having elevators wait during times when residents frequently used them in the past. It can also support quicker travel by having elevators wait on floors that residents have used in the past. Furthermore, it can analyze specific patterns from residents' usage history and adjust elevator operation based on those patterns. In this way, convenience can be improved by optimizing elevator operation based on residents' past usage history.

[0176] The following briefly describes the processing flow for example form 2.

[0177] Step 1: The lifestyle rhythm learning unit learns the residents' lifestyle patterns. For example, it collects data such as residents' wake-up times, return times, and frequency of going out, and learns lifestyle patterns based on this data. Using AI, it analyzes the residents' lifestyle patterns and provides information to set the optimal waiting floor. Step 2: The standby floor adjustment unit sets the optimal standby floor based on the lifestyle patterns learned by the lifestyle rhythm learning unit. For example, by having the elevator wait on a specific floor during times when residents frequently use it, waiting times are reduced. AI is used to dynamically adjust the standby floor based on residents' lifestyle patterns. Step 3: The visitor service department prioritizes elevator operation based on the visitor's needs. For example, when a visitor arrives, the elevator will be made to wait on the first floor to ensure a smooth arrival. AI is used to adjust priority operation based on the importance and time of day of the visitor. Step 4: The cargo handling department adjusts operations during cargo delivery. For example, it adjusts elevator operations during large-scale cargo deliveries to avoid disrupting other residents' use. AI is used to adjust operations based on the type of cargo and delivery time. Step 5: The security enhancement department implements access control using facial recognition. For example, it recognizes residents' faces to restrict elevator use and prevent intruders from entering. AI is used to improve the accuracy of facial recognition and optimize the authentication process. Step 6: The energy-saving operations department optimizes energy efficiency. For example, it saves energy by automatically turning off air conditioning and lighting during times when elevators are not in use. AI is used to reduce power consumption and adjust the operation schedule.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the lifestyle rhythm learning unit, standby floor adjustment unit, visitor handling unit, package handling unit, security enhancement unit, and energy-saving operation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the lifestyle rhythm learning unit is implemented by the processor 46 of the smart device 14 and learns the residents' lifestyle patterns. The standby floor adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets the optimal standby floor based on the learned lifestyle patterns. The visitor handling unit is implemented by the control unit 46A of the smart device 14 and performs priority operation according to the visitors. The package handling unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts operation when packages are being delivered. The security enhancement unit is implemented by the camera 42 and control unit 46A of the smart device 14 and performs access control by facial recognition. The energy-saving operation unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes energy efficiency. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] Each of the multiple elements described above, including the lifestyle rhythm learning unit, standby floor adjustment unit, visitor handling unit, luggage handling unit, enhanced security unit, and energy-saving operation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the lifestyle rhythm learning unit is implemented by the processor 46 of the smart glasses 214 and learns the residents' lifestyle patterns. The standby floor adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sets the optimal standby floor based on the learned lifestyle patterns. The visitor handling unit is implemented by, for example, the control unit 46A of the smart glasses 214 and performs priority operation according to the visitors. The luggage handling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts operation when luggage is being delivered. The enhanced security unit is implemented by, for example, the camera 42 and control unit 46A of the smart glasses 214 and performs access control by facial recognition. The energy-saving operation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes energy efficiency. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0198] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] Each of the multiple elements described above, including the lifestyle rhythm learning unit, standby floor adjustment unit, visitor handling unit, luggage handling unit, security enhancement unit, and energy-saving operation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the lifestyle rhythm learning unit is implemented by the processor 46 of the headset terminal 314 and learns the residents' lifestyle patterns. The standby floor adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets the optimal standby floor based on the learned lifestyle patterns. The visitor handling unit is implemented by the control unit 46A of the headset terminal 314 and performs priority operation according to the visitors. The luggage handling unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts operation when luggage is being delivered. The security enhancement unit is implemented by the camera 42 and control unit 46A of the headset terminal 314 and performs access control by facial recognition. The energy-saving operation unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes energy efficiency. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0214] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] Each of the multiple elements described above, including the lifestyle rhythm learning unit, standby floor adjustment unit, visitor handling unit, luggage handling unit, enhanced security unit, and energy-saving operation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the lifestyle rhythm learning unit is implemented by the processor 46 of the robot 414 and learns the residents' lifestyle patterns. The standby floor adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sets the optimal standby floor based on the learned lifestyle patterns. The visitor handling unit is implemented by, for example, the control unit 46A of the robot 414 and performs priority operation according to visitors. The luggage handling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts operation when luggage is being delivered. The enhanced security unit is implemented by, for example, the camera 42 and control unit 46A of the robot 414 and performs access control by facial recognition. The energy-saving operation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes energy efficiency. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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."

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] (Note 1) The Lifestyle Rhythm Learning Department learns about the lifestyle patterns of residents, A standby floor adjustment unit sets the optimal standby floor based on the lifestyle pattern learned by the lifestyle rhythm learning unit, The Visitors Department provides priority service tailored to the needs of visitors, The cargo handling department is responsible for coordinating operations during cargo delivery, A security enhancement unit that uses facial recognition for access control, It includes an energy-saving operation unit that optimizes energy efficiency. A system characterized by the following features. (Note 2) It is equipped with an assist unit that provides support functions for the elderly and people with disabilities. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a settings section for customizing shared floor preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The facility has a train operations planning department that optimizes train schedules to match rush hour and return-home times. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with a security mode section that implements nighttime security mode. The system described in Appendix 1, characterized by the features described herein. (Note 6) It is equipped with an adjustment unit that automatically adjusts the air conditioning and lighting. The system described in Appendix 1, characterized by the features described herein. (Note 7) It features a notification unit that provides real-time information on waiting times and congestion levels via a smartphone app. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned Lifestyle Rhythm Learning Department The system estimates residents' emotions and improves the accuracy of learning their daily routines based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned Lifestyle Rhythm Learning Department Analyzing residents' past behavioral history to predict changes in their daily routines. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned Lifestyle Rhythm Learning Department The elevator's waiting floor is dynamically adjusted based on the residents' daily routines. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned Lifestyle Rhythm Learning Department The system estimates the residents' emotions and adjusts the learning frequency of their daily routines based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned Lifestyle Rhythm Learning Department Optimize elevator operation schedules based on residents' daily routines. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned Lifestyle Rhythm Learning Department Adjust elevator maintenance schedules based on residents' daily routines. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned standby floor adjustment unit is The system estimates the residents' emotions and adjusts the standby floor settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned standby floor adjustment unit is When setting the standby floor, the system selects the optimal standby floor by referring to the residents' past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned standby floor adjustment unit is When setting the standby floor, the standby floor is dynamically changed considering the current living situation of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned standby floor adjustment unit is The system estimates the residents' emotions and determines the priority of waiting floors based on those estimated emotions. The system according to Appendix 1, characterized in that... (Appendix 18) The standby level adjustment unit When setting the standby level, selects the optimal standby level considering the geographical location information of the residents The system according to Appendix 1, characterized in that... (Appendix 19) The standby level adjustment unit When setting the standby level, analyzes the social media activities of the residents and adjusts the standby level The system according to Appendix 1, characterized in that... (Appendix 20) The visitor response unit Estimates the emotions of the residents and adjusts the method of visitor response based on the estimated emotions of the residents The system according to Appendix 1, characterized in that... (Appendix 21) The visitor response unit When dealing with visitors, selects the optimal response method by referring to the past visitor history of the residents The system according to Appendix 1, characterized in that... (Appendix 22) The visitor response unit When dealing with visitors, customizes the response method considering the current living situation of the residents The system according to Appendix 1, characterized in that... (Appendix 23) The visitor response unit Estimates the emotions of the residents and determines the priority order of visitor response based on the estimated emotions of the residents The system according to Appendix 1, characterized in that... (Appendix 24) The visitor response unit [[ID=5,3]] When dealing with visitors, selects the optimal response method considering the geographical location information of the residents The system according to Appendix 1, characterized in that... (Appendix 25) The visitor response unit When dealing with visitors, analyzes the social media activities of the residents and adjusts the response method The system according to Appendix 1, characterized in that... (Appendix 26) The luggage handling unit estimates the emotions of the residents and adjusts the luggage handling method based on the estimated emotions of the residents The system according to Appendix 1, characterized in that it does so. (Appendix 27) The luggage handling unit selects an optimal handling method by referring to the past luggage handling history of the residents when handling luggage The system according to Appendix 1, characterized in that it does so. (Appendix 28) The luggage handling unit customizes the handling method by considering the current living situation of the residents when handling luggage The system according to Appendix 1, characterized in that it does so. (Appendix 29) The luggage handling unit estimates the emotions of the residents and determines the priority of luggage handling based on the estimated emotions of the residents The system according to Appendix 1, characterized in that it does so. (Appendix 30) The luggage handling unit selects an optimal handling method by considering the geographical location information of the residents when handling luggage The system according to Appendix 1, characterized in that it does so. (Appendix 31) The luggage handling unit analyzes the social media activities of the residents and adjusts the handling method when handling luggage The system according to Appendix 1, characterized in that it does so. (Appendix 32) The crime prevention enhancement unit estimates the emotions of the residents and adjusts the crime prevention enhancement method based on the estimated emotions of the residents The system according to Appendix 1, characterized in that it does so. (Appendix 33) The crime prevention enhancement unit selects an optimal crime prevention method by referring to the past crime prevention history of the residents when enhancing crime prevention The system according to Appendix 1, characterized in that it does so. (Appendix 34) The aforementioned security enhancement unit is, When strengthening security measures, customize security methods to take into account the current living conditions of residents. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned security enhancement unit is, The system estimates the sentiments of residents and determines the priority of crime prevention measures based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned security enhancement unit is, When strengthening crime prevention measures, the most suitable crime prevention method will be selected considering the geographical location information of residents. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned security enhancement unit is, When strengthening crime prevention measures, analyze residents' social media activity and adjust crime prevention methods accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned energy-saving operation unit is The system estimates the sentiments of residents and adjusts energy-saving operation methods based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned energy-saving operation unit is During energy-saving operation, the system selects the optimal energy-saving method by referring to residents' past energy consumption history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned energy-saving operation unit is During energy-saving operation, energy-saving methods are customized to take into account the current living conditions of residents. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned energy-saving operation unit is The system estimates residents' sentiments and determines energy-saving operational priorities based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 42) The energy-saving operation unit selects an optimal energy-saving method considering the geographical location information of residents during energy-saving operation. The system according to Supplementary Note 1, characterized in that. (Supplementary Note 43) The energy-saving operation unit analyzes the social media activities of residents and adjusts the energy-saving method during energy-saving operation. The system according to Supplementary Note 1, characterized in that. (Supplementary Note 44) The assistance unit estimates the emotions of residents and adjusts the method of providing assistance functions based on the estimated emotions of residents. The system according to Supplementary Note 2, characterized in that. (Supplementary Note 45) The assistance unit selects an optimal assistance method by referring to the past usage history of residents when providing assistance functions. The system according to Supplementary Note 2, characterized in that. (Supplementary Note 46) The assistance unit estimates the emotions of residents and determines the priority order of assistance functions based on the estimated emotions of residents. The system according to Supplementary Note 2, characterized in that. (Supplementary Note 47) The assistance unit selects an optimal assistance method considering the geographical location information of residents when providing assistance functions. The system according to Supplementary Note 2, characterized in that. (Supplementary Note 48) The setting unit estimates the emotions of residents and adjusts the setting method based on the estimated emotions of residents. The system according to Supplementary Note 3, characterized in that. (Supplementary Note 49) The setting unit selects an optimal setting method by referring to the past setting history of residents during setting. The system according to Supplementary Note 3, characterized in that. (Supplementary Note 50) The aforementioned setting unit is, The system estimates the residents' sentiments and determines the priority of settings based on those estimated sentiments. The system described in Appendix 3, characterized by the features described herein. (Note 51) The aforementioned setting unit is, During setup, the optimal setup method is selected considering the geographical location information of residents. The system described in Appendix 3, characterized by the features described herein. (Note 52) The aforementioned operation planning department, We estimate the sentiments of the residents and adjust the operational planning method based on those estimated sentiments. The system described in Appendix 4, characterized by the features described herein. (Note 53) The aforementioned operation planning department, When planning the route, the optimal route plan is selected by referring to the residents' past route history. The system described in Appendix 4, characterized by the features described herein. (Note 54) The aforementioned operation planning department, The system estimates residents' sentiments and determines the priority of the transportation plan based on those estimated sentiments. The system described in Appendix 4, characterized by the features described herein. (Note 55) The aforementioned operation planning department, When planning train operations, the optimal operation plan is selected by taking into account the geographical location information of residents. The system described in Appendix 4, characterized by the features described herein. (Note 56) The security mode unit is, The system estimates residents' sentiments and adjusts security mode based on those estimated sentiments. The system described in Appendix 5, characterized by the features described herein. (Note 57) The security mode unit is, When security mode is activated, the system selects the optimal security method by referring to the resident's past security history. The system described in Appendix 5, characterized by the features described herein. (Note 58) The security mode unit is, When security mode is activated, the security method is customized to take into account the current living situation of the residents. The system described in Appendix 5, characterized by the features described herein. (Note 59) The security mode unit is, The system estimates residents' sentiments and determines security mode priorities based on those estimated sentiments. The system described in Appendix 5, characterized by the features described herein. (Note 60) The security mode unit is, When security mode is implemented, the optimal security method is selected considering the geographical location information of residents. The system described in Appendix 5, characterized by the features described herein. (Note 61) The security mode unit is, When security mode is implemented, the system analyzes residents' social media activity and adjusts security measures accordingly. The system described in Appendix 5, characterized by the features described herein. (Note 62) The adjustment unit is, We estimate the residents' feelings and adjust the adjustment method based on the estimated residents' feelings. The system described in Appendix 6, characterized by the features described herein. (Note 63) The adjustment unit is, During the adjustment process, the optimal adjustment method will be selected by referring to the residents' past adjustment history. The system described in Appendix 6, characterized by the features described herein. (Note 64) The adjustment unit is, Estimate the sentiments of the residents and determine the priority of adjustments based on those estimated sentiments. The system described in Appendix 6, characterized by the features described herein. (Note 65) The adjustment unit is, During the adjustment process, the optimal adjustment method will be selected, taking into account the geographical location information of the residents. The system described in Appendix 6, characterized by the features described herein. (Note 66) The aforementioned notification unit, We estimate the sentiments of residents and adjust the notification method based on those estimated sentiments. The system described in Appendix 7, characterized by the features described herein. (Note 67) The aforementioned notification unit, When sending a notification, the system will refer to the resident's past notification history to select the most suitable notification method. The system described in Appendix 7, characterized by the features described herein. (Note 68) The aforementioned notification unit, The system estimates residents' sentiments and determines the priority of notifications based on those estimated sentiments. The system described in Appendix 7, characterized by the features described herein. (Note 69) The aforementioned notification unit, When sending notifications, the most suitable notification method will be selected, taking into account the residents' geographical location information. The system described in Appendix 7, characterized by the features described herein. [Explanation of Symbols]

[0250] 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 Lifestyle Rhythm Learning Department learns about the lifestyle patterns of residents, A standby floor adjustment unit sets the optimal standby floor based on the lifestyle pattern learned by the lifestyle rhythm learning unit, The Visitors Department provides priority service tailored to the needs of visitors, The cargo handling department is responsible for coordinating operations during cargo delivery, A security enhancement unit that uses facial recognition for access control, It includes an energy-saving operation unit that optimizes energy efficiency. A system characterized by the following features.

2. It is equipped with an assist unit that provides support functions for the elderly and people with disabilities. The system according to feature 1.

3. It features a settings section for customizing shared floor preferences. The system according to feature 1.

4. The facility has a train operations planning department that optimizes train schedules to match rush hour and return-home times. The system according to feature 1.

5. It is equipped with a security mode section that implements nighttime security mode. The system according to feature 1.

6. It is equipped with an adjustment unit that automatically adjusts the air conditioning and lighting. The system according to feature 1.

7. It features a notification unit that provides real-time information on waiting times and congestion levels via a smartphone app. The system according to feature 1.

8. The aforementioned Lifestyle Rhythm Learning Department The system estimates residents' emotions and improves the accuracy of learning their daily routines based on those estimated emotions. The system according to feature 1.

Images