system

Abstract

We provide the system. [Solution] A means of sending notifications to optimize the user's progress, A means of determining location using the location information of the user's terminal, A means of accessing a cloud-based data management system to retrieve schedule and operational information, A means of identifying users using facial recognition technology, A means of sending notifications to optimize elevator usage, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional elevator system, there is a problem that it takes time every time the user moves because the user has to press a button after arriving at the elevator hall and manually set the destination floor. In particular, in a smart office environment, efficient and rapid movement is required, but it has been difficult to achieve further efficiency in moving between floors and when returning home with the conventional systems. The object of this invention is to solve these problems by reducing the time loss of the user and smoothing the overall movement process.

Means for Solving the Problems

[0005] This invention analyzes user movements by combining means for acquiring user schedule information and predicting the next destination with means for performing facial recognition using video data from an imaging device installed in a corridor to identify the user. Furthermore, based on the predicted destination, it enables efficient elevator use by pre-calling an elevator and automatically pressing the designated "up / down buttons" as a specified operation. In addition, when a user boards the elevator, the destination floor is automatically set, the optimal elevator is selected, and the need for button operation is eliminated. This series of procedures significantly improves the efficiency of travel.

[0006] "User schedule information" refers to the date, time, and location of meetings and appointments that a specific user has recorded as future plans.

[0007] An "imaging device" is a device that has the function of acquiring images in digital format, and includes devices such as cameras.

[0008] "Facial recognition" is a technology that analyzes a person's face from video data and identifies that person.

[0009] The "predicted destination" is the floor that the system estimates as the user's next destination, based on the acquired schedule information and facial recognition results.

[0010] "Automatic elevator setting" refers to an operation in elevators where the system automatically selects the necessary floor to reach the user's destination without user intervention.

[0011] An "elevator hall" is a common area where users stop while waiting for the elevator, and it is usually located on each floor. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention relates to an elevator system that supports the efficient movement of users, and a specific embodiment thereof is described below.

[0034] First, the server retrieves user schedule information from the company's internal database. This includes information related to the next floor the user should move to, such as the location and time of scheduled meetings. The schedule information is pre-entered by the user according to their daily activities.

[0035] Next, imaging devices, or terminals, installed in elevator lobbies and corridors on each floor capture video at regular intervals and transmit this video data to a server. The server uses facial recognition technology to identify users from the captured video and, by linking this information with schedule information, predicts the user's next destination.

[0036] Based on this prediction, the server considers the current elevator layout and operating status to select the most suitable elevator. For the selected elevator, the server automatically sends a signal to press the appropriate "up / down button," allowing the user to board immediately upon arriving at the elevator hall.

[0037] The terminal, acting as part of the elevator's control panel, receives instructions from the server and automatically sets the destination floor after the user enters the elevator. Therefore, users do not need to press any buttons when entering the elevator, allowing them to reach their desired floor smoothly.

[0038] As a concrete example, consider the case where user A moves from the 5th floor to a conference room on the 12th floor. The server identifies the next destination as the 12th floor from user A's schedule information and predicts that user A will head to the 12th floor when user A is detected by a nearby camera. Based on this information, the server calls an elevator heading towards the 12th floor, and since the destination floor is already set to the 12th floor when the elevator arrives, user A can arrive at their destination immediately.

[0039] In this form of the invention, user movement can be optimized and time can be used efficiently. This will lead to further improvements in work efficiency in a smart office environment.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server periodically retrieves schedule information for all users from the company's internal database. It collects data on upcoming meetings and appointments for each user and organizes it accordingly.

[0043] Step 2:

[0044] Imaging devices installed in corridors and elevator lobbies, which function as terminals, capture real-time video data at regular intervals and transmit it to a server. This video data captures the movements of users within the building.

[0045] Step 3:

[0046] The server uses facial recognition technology on the received video data to identify people in the video. The identified person information is then compared with previously collected schedule information and used to predict their movements.

[0047] Step 4:

[0048] The server compares the identified user's schedule information with the current time to predict the next floor the user should go to. This prediction serves as the basic data for elevator call operations.

[0049] Step 5:

[0050] The server checks the current location and operating status of the elevators and selects the most efficient elevator. The server then sends a signal to the selected elevator to press either the up or down button. This prepares the elevator to proceed to the user's desired floor.

[0051] Step 6:

[0052] The terminal (the control panel inside the elevator) automatically sets the predicted destination floor when a user enters the elevator, following instructions from the server. This automation eliminates the need for passengers to manually press buttons.

[0053] Step 7:

[0054] The user boards the elevator and confirms that it is set to the expected destination floor. The user can then exit the elevator without pressing any buttons and arrive at their destination smoothly. This entire system process ensures efficient travel.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In office environments where efficient movement is essential, there is a need for means to reduce user effort and reach destinations quickly and accurately. Conventional elevator systems require users to manually select their destination floor, which is cumbersome and can reduce the overall efficiency of the process. Furthermore, during peak hours, multiple people boarding and alighting simultaneously makes optimal elevator use difficult.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for acquiring user activity information and estimating the next destination, means for acquiring photographic data from an installed recording device and utilizing identification technology, and means for controlling an elevator and automatically performing specific operations based on the estimated destination. This enables users to reach their destination level quickly and accurately without manual operation.

[0060] "Users" refers to individual people who travel using the elevator system.

[0061] "Activity information" refers to data about the user's plans and schedules, and is used to estimate their next destination.

[0062] "Destination" refers to the place the user plans to go to next.

[0063] A "recording device" refers to a machine or device installed to acquire image or video data.

[0064] "Shooting data" refers to image and video information acquired by a recording device.

[0065] "Identification technology" refers to technology that identifies specific individuals from video data using methods such as facial recognition.

[0066] A "lift" refers to a transport device used to move users between different floors, in other words, an elevator.

[0067] "Control" refers to adjusting the operation and position of elevators and escalators to properly manage the movement of users to their destinations.

[0068] This invention is an elevator system designed to support the efficient movement of users. This system combines multiple technical elements to enable users to reach their destination quickly and accurately. Specific embodiments are described below.

[0069] The server connects to the company's internal database to retrieve user activity information. This activity information includes the location and time of meetings and appointments, serving as basic data for estimating the next destination. Users store their information in this database by registering their schedules in the system in advance.

[0070] Recording devices installed on each floor and in elevator lobbies are used as terminals. These devices capture video at regular intervals and transmit the captured data to a server. The server uses identification technology on the video data, identifying users using software such as OpenCV and FaceNet. This makes it possible to link user identification information with activity information, allowing for accurate prediction of where users will go next.

[0071] Next, the server controls the optimal elevator based on the predicted destination, taking into account the current operating status and location information of the elevators within the system. Here, the server sends appropriate instruction signals to the elevators, ensuring that the destination floor is pre-set. This allows users to move smoothly to their destination floor as soon as they board the elevator.

[0072] As a concrete example, consider a scenario where a user moves from the 5th floor to a conference room on the 12th floor. The server estimates from the user's activity information that the conference on the 12th floor is the next destination and recognizes that the user is on the 5th floor. The server then selects the appropriate elevator and automatically adjusts it to head to the 12th floor. Once the user gets on the elevator, they can travel directly to the 12th floor.

[0073] A concrete example of a prompt message would be: "Please explain the mechanism that predicts the next destination based on the user's activity information, automatically arranges the most suitable elevator, and sets it to the destination floor."

[0074] This system contributes to the realization of a smart office environment by minimizing manual operation by users and providing efficiency and comfort.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server accesses the company's internal database to retrieve user activity information. The input is schedule data from the database, and the output is information about each user's next destination. This information is processed by extracting the necessary data using SQL queries or similar methods.

[0078] Step 2:

[0079] The terminal captures video at regular intervals using recording devices installed in the elevator hall and on each floor. The input is raw video data captured by the camera, and the output is image data sent to the server. This image data is compressed and sent to the server via the network.

[0080] Step 3:

[0081] The server applies identification technology to the received image data to identify the user. The input is video data, and the output is the identification information of the identified user. This process uses OpenCV or FaceNet for facial recognition to extract the ID of a specific person.

[0082] Step 4:

[0083] The server matches user activity information based on identification information and predicts the next destination. The input is identification information and schedule information, and the output is the estimated next destination. At this stage, schedule matching is performed through data calculation to determine the most suitable next destination.

[0084] Step 5:

[0085] The server selects the most efficient elevator, taking into account its operational status and current location. The input is the elevator's current dispatch information and destination data, and the output is the ID of the selected elevator. An algorithm is used to evaluate the shortest route and operational efficiency to make the optimal selection.

[0086] Step 6:

[0087] The server sends appropriate instructions to the selected elevator. The input is the elevator ID and destination floor information, and the output is a control signal. The control signal instructs the elevator to automatically move to the target floor.

[0088] Step 7:

[0089] When a user boards an elevator, it automatically moves to a pre-set destination. No manual user input is required; the input is the boarding event, and the output is arrival at the destination floor. This allows users to reach their designated destination quickly and efficiently.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In modern urban environments and commercial facilities, the efficient use of elevators is essential due to the large number of people moving around at once. However, existing systems struggle to accurately identify the user's position and select the most suitable elevator. Furthermore, requiring users to choose an elevator and operate buttons themselves leads to wasted time. The challenge lies in solving these problems and ensuring smoother user movement.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for sending notifications to optimize the user's route, means for determining the user's location using the user's terminal location information, and means for accessing a cloud-based data management system to obtain schedule and operational information. This makes the user's route to their destination more efficient, reducing elevator waiting times and unnecessary movement, and enabling smoother travel.

[0095] "Notifications to optimize user routes" are messages that inform users of the optimal route and elevator information on their devices in order to help them move around the building efficiently.

[0096] "Means of determining location using location information of the user's terminal" refers to technologies that accurately determine the user's current location by utilizing the built-in GPS or beacon signals of mobile information terminals such as smartphones.

[0097] A "cloud-based data management system" is a distributed information processing system that collects and analyzes user data and elevator operation information via the internet, and provides necessary information quickly.

[0098] "Methods for identifying users using facial recognition technology" refers to technologies that analyze video data acquired using installed cameras to identify users.

[0099] "A means of sending notifications to optimize elevator use" refers to technology that sends information about elevator availability and the shortest route to the user's device, thereby making travel more efficient.

[0100] The system of this invention is built to support the efficient movement of users by integrating location information, facial recognition, and notification technologies. The server acquires users' schedule information and elevator operating status within buildings in real time through a cloud-based data management system. This allows the system to calculate the optimal route for each user and send necessary notifications.

[0101] The term "terminal" refers to a mobile information device such as a smartphone, which uses its built-in GPS to determine the user's current location. This allows the system to immediately receive optimal route information from the server and display it visually to the user.

[0102] This system incorporates facial recognition technology, allowing users to be identified by installed cameras. This information is then cross-referenced with a schedule by a server to ensure accurate notifications. Specifically, the server uses technology to optimize elevator usage and send notifications. This allows users to reach their destinations efficiently while minimizing elevator waiting times.

[0103] For example, if a user is a busy businessman with a series of meetings, the device's notification system will proactively call the next elevator he needs, helping him reach his destination in the shortest possible time. In this system, elevator availability is monitored by a cloud-based server, and the system is optimized based on the user's location and schedule.

[0104] An example of a prompt for a generated AI model is: "Design an application that allows users to efficiently move between multiple meeting rooms in a business building, and use facial recognition and scheduling data to indicate the optimal route." This prompt allows the model to devise an efficient mobility assistance system.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The user enables location services on a device such as a smartphone. The device uses its built-in GPS sensor to obtain the user's current location. This information is transmitted to the server via the device's communication module. The input is the device's location data, and the output is the transmission of the user's location information to the server.

[0108] Step 2:

[0109] The server uses a cloud-based data management system to retrieve user schedule information and elevator operating status within the building. Input is request data based on the user ID, and output is schedule information related to the current time and real-time elevator information.

[0110] Step 3:

[0111] The server uses a facial recognition algorithm to process video data acquired from installed cameras to identify users. The input is video data, and the output is user identification information. This identification information is then cross-referenced with the schedule.

[0112] Step 4:

[0113] The server calculates the most efficient elevator route based on user identification information, schedule, and elevator operating status. The inputs are identified user information, schedule, and operating status, and the output is the specified optimal elevator route.

[0114] Step 5:

[0115] The server sends the calculated optimal elevator route as a notification to the user's terminal. The input is the optimized route information, and the output is the notification message sent to the user's terminal.

[0116] Step 6:

[0117] The user follows the received notification and heads to the designated elevator. The terminal displays the next floor and directions as needed. The input is the notification content, and the output is the user's action.

[0118] Step 7:

[0119] Inside the elevator, the server automatically sets the elevator's destination based on a pre-configured destination floor. The input is the user's destination information, and the output is an instruction to the elevator control system.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention relates to a system that incorporates an emotion engine into an elevator system to provide flexible services that respond to the emotions of users. The embodiments thereof will be described in detail below.

[0122] First, in addition to conventional scheduling information and facial recognition functions, the server is equipped with an emotion engine for analyzing users' emotions in real time. This emotion engine analyzes video data acquired from terminals (imaging devices) installed in corridors and elevator lobbies, detecting users' facial expressions and voice tone to identify their emotional state.

[0123] Based on the results analyzed by the emotion engine, the server will instruct priority actions to reduce elevator waiting times if it determines that the user is stressed or in a hurry. Conversely, if the user is relaxed, normal operation will continue.

[0124] Furthermore, the server sends instructions to the control terminal inside the elevator to adjust the environment within the elevator. For example, if emotion analysis determines that a user is feeling tense, the lighting inside the elevator will be softened and relaxing music will be played to ensure the user is comfortable.

[0125] As a concrete example, consider a scenario where User B is rushing to a meeting. Based on the meeting schedule information, the server predicts User B's movement. The emotion engine analyzes the footage captured by the elevator hall camera and, if it detects that User B is slightly anxious, the server quickly optimizes the elevator and performs the action of pressing the "up button" earlier than usual.

[0126] When the elevator arrives and user B boards, music is played and the lighting is adjusted to provide a relaxing environment. This approach provides a service that is considerate of the user's feelings, enabling a more comfortable and efficient journey.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server retrieves user schedule information from the building's database. This information includes the user's next destination and the time of their arrival.

[0130] Step 2:

[0131] The terminal (imaging device) captures video data of users in elevator lobbies and corridors and transmits it to the server in real time. This data is used for subsequent facial recognition and emotion analysis.

[0132] Step 3:

[0133] The server uses the transmitted video data to perform facial recognition and identify specific users. This information is then cross-referenced with the user's schedule information.

[0134] Step 4:

[0135] The server uses an emotion engine to analyze the user's emotional state from the transmitted video data. It determines whether the user is in a hurry, relaxed, or otherwise, based on changes in facial expressions and tone of voice.

[0136] Step 5:

[0137] The server adjusts the elevator's operation based on the results of emotion analysis. For example, if it determines that a user is in a hurry, the server will prioritize calling the elevator and issue instructions to reduce waiting time.

[0138] Step 6:

[0139] The terminal (the control unit inside the elevator) automatically sets the destination floor when a user boards, based on instructions from the server. It also adjusts the lighting and music based on the user's mood at the time of boarding, providing a comfortable environment. This operation enhances the user's elevator experience.

[0140] Step 7:

[0141] The user boards the elevator and travels to their pre-set destination floor. The comfortable environment and efficient travel allow the user to reach their destination without stress.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] Conventional vertical transport systems have struggled to provide flexible services that take into account users' emotions and schedules, making it difficult to achieve efficient and comfortable travel. In particular, they lacked the ability to accommodate time-sensitive travel situations and to create an environment that suits users' emotional needs.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for acquiring the user's scheduled time information and predicting the next destination, means for acquiring visual information from image acquisition devices installed along the route and performing personal recognition, and means for analyzing the user's emotional state and adjusting the operation of the vertical transport device. This makes it possible to provide a more comfortable and efficient travel service that is tailored to the user's schedule and emotions.

[0147] "Users" refer to individuals who utilize this system and are the recipients of services based on their scheduled time and emotional state.

[0148] "Scheduled time information" refers to information that shows the user's schedule and planned activities, and serves as basic data for predicting the next destination.

[0149] An "image acquisition device" is a device installed along a route or within a facility to acquire visual information and provide data for personal identification.

[0150] "Personal recognition" refers to technology that identifies users based on acquired visual information and analyzes their emotional state.

[0151] "Emotional state" refers to the state in which a user exhibits emotional responses, including emotions such as smiling, anger, anxiety, and relaxation.

[0152] A "vertical transport system" is a mechanical device used to move users between different floor levels, and is commonly known as an elevator.

[0153] "Means of adjusting the environment" refers to methods and technologies for adjusting lighting, music, and other elements inside vertical transport systems to provide users with a comfortable environment.

[0154] This system is an elevator system that provides flexible service tailored to the emotional state of the user. Specifically, a server and multiple terminals work together to collect and analyze user information, and adjust the elevator's operation and internal environment accordingly.

[0155] First, the terminal uses cameras installed in elevator lobbies and corridors to acquire video data of users in real time. This video data is sent to a server and analyzed by an emotion engine on the server. The emotion engine uses image processing libraries such as OpenCV and machine learning frameworks such as TENSORFLOW® to analyze the user's facial expressions and tone of voice to identify their emotional state.

[0156] Furthermore, the server retrieves time schedule information from the user's smart device or IC card and compares it with the current schedule. Based on this information, it predicts the next destination and determines whether the user is in a hurry.

[0157] The server optimizes elevator operation and adjusts the environment inside the elevator according to the user's emotional state and schedule information. For example, if it determines that the user is stressed, it adjusts the elevator lighting to a warm color and plays relaxation music. This adjustment is intended to provide users with a comfortable and relaxing environment.

[0158] For example, if a user is rushing to a meeting, the server analyzes the video data from the camera using an emotion engine to detect that the user is anxious. The server then minimizes elevator waiting times and performs quick operations to help the user reach their destination smoothly.

[0159] An example of a prompt message would be, "How should the vertical transport system respond when the user is in a hurry?"

[0160] In this way, this system can provide more comfortable and efficient transportation services based on the user's emotional state.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The terminal uses cameras installed in elevator lobbies and corridors to acquire real-time video data of users. These cameras are configured to capture multiple users simultaneously and transmit the video data to a server. The input is video data, and the output is a digital video signal transmitted to the server.

[0164] Step 2:

[0165] The server receives video data transmitted from the terminal and begins analysis using an emotion engine. Here, OpenCV is used to detect the position and features of the face, and TensorFlow is used to identify the emotional state from facial expressions and voice tone. The input is the video signal from the terminal, and the output is data indicating the user's emotional state.

[0166] Step 3:

[0167] The server retrieves time schedule information from the user's smart device or IC card. This includes obtaining the latest schedule data. The input is time schedule data from the smart device, and the output is basic data for predicting the user's next destination.

[0168] Step 4:

[0169] The server combines the results of sentiment analysis with scheduled time information to evaluate the user's current state. It processes this data to determine the situation, such as whether the user is in a hurry or relaxed, and decides whether priority elevator service is needed. The inputs are sentiment state and scheduled time information, and the output is instruction data necessary for service optimization.

[0170] Step 5:

[0171] The server uses the generated instruction data to reset elevator waiting times and routes. In some cases, it issues instructions for emergency priority actions to optimize elevator operation. The input is instruction data, and the output is elevator control signals.

[0172] Step 6:

[0173] When a user enters an elevator, the server issues instructions to adjust the elevator's internal environment. This includes specific actions such as adjusting the color temperature of the lighting and setting appropriate background music. The input is the user's emotional state, and the output is the adjusted elevator environment.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0176] Conventional mobility systems struggle to provide flexible services that take into account the user's emotional state and schedule information, hindering user satisfaction and efficient travel. Furthermore, there is a challenge in that environmental adjustments to reduce user stress and anxiety are not automated.

[0177] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0178] In this invention, the server includes means for acquiring user schedule information and predicting the next destination, means for acquiring video data from an imaging device installed in the passageway and performing emotion analysis, means for calling a mobile device based on the predicted destination and automatically executing a specified operation, and means for adjusting the riding environment according to the emotional state of the user during the ride. This enables the provision of flexible services that respond to the user's emotions and the realization of efficient and comfortable travel.

[0179] A "user" refers to any human being who uses the system.

[0180] "Schedule information" refers to data that shows the user's schedule and activity plan.

[0181] "Next destination" refers to the place the user plans to go to next.

[0182] A "passageway" is a route used for movement inside or outside a building.

[0183] An "imaging device" is a device used to acquire images or videos.

[0184] "Video data" refers to the digital data of acquired images and videos.

[0185] "Emotional analysis" is the process of identifying an emotional state by analyzing characteristics such as facial expressions and voice.

[0186] "Mobility devices" refer to equipment used to transport people, such as elevators and escalators.

[0187] "Specified operation" refers to actions or procedures that the system has pre-configured.

[0188] "Riding environment" refers to environmental elements that users perceive within the vehicle, such as lighting and music.

[0189] To realize this invention, the system is configured as follows: The server acquires schedule information using a small computer such as a Raspberry Pi or AWS® cloud service and predicts the next destination. Terminals installed in the corridor are equipped with a camera (such as a Logitech C920) and a microphone, and acquire video and audio data through face recognition using OpenCV and speech recognition using Google® Cloud Speech-to-Text API. This allows the system to identify the emotional state of the user from their facial expressions and voice using an emotion analysis library.

[0190] Based on the emotion analysis, the server determines the user's emotional state and, based on that, summons a mobility device (e.g., an elevator or a household robot) and automatically performs the specified operation. Furthermore, the server adjusts the ride environment, playing music and adjusting the lighting to ensure the user's comfort.

[0191] As a concrete example, when a user returns home tired, the lighting in the vehicle is immediately adjusted to a relaxing level, and soothing music is played. This reduces the stress that users experience in their daily lives and provides a more comfortable environment.

[0192] An example of a prompt for a generative AI model is, "Please give me some ideas for programming an application that will enable a home robot to recognize the user's emotions and provide appropriate support." Using this prompt, insights can be gained regarding the development of robot applications with emotion recognition capabilities.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server retrieves user schedule information from the database. It receives a user ID as input and retrieves the appointments and activity plans associated with that ID. As output, it obtains schedule information that serves as the basis for predicting the user's next destination. Based on this information, it calculates the predicted destination the user will take.

[0196] Step 2:

[0197] The terminal acquires video data using cameras installed in the corridor. It receives real-time video as input and performs face recognition using OpenCV. As output, it identifies the user's face and extracts feature vectors from the face image. These feature vectors are used later for sentiment analysis.

[0198] Step 3:

[0199] The device acquires audio data using a microphone. As input, it records the user's voice in real time and converts it to text using the Google Cloud Speech-to-Text API. As output, text data is generated, and its tone is used for sentiment analysis. This audio data also contributes to identifying emotional states.

[0200] Step 4:

[0201] The server uses an emotion analysis library to identify the user's emotional state from the facial features and audio text data obtained in steps 2 and 3. It receives facial features and voice tone as input and performs analysis using an emotion model. The identified emotional state is obtained as output. This allows for an understanding of the user's current mood and psychological state.

[0202] Step 5:

[0203] The server uses emotion analysis results and schedule information to summon and automate the operation of the mobile device. It selects the optimal mobile device using predicted destinations and emotional state information as input. The output includes summoning the mobile device and adjusting its waiting time. This operation reduces user stress and efficiently guides them to their destination.

[0204] Step 6:

[0205] When a user boards a mobile device, the server sends instructions to adjust the environment. It receives an identified emotional state as input and determines music and lighting settings accordingly. As output, the environment within the mobile device is automatically adjusted, providing a comfortable space for the user. This operation may further improve the user's mood.

[0206] 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.

[0207] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0213] 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.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] 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.

[0217] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] This invention relates to an elevator system that supports the efficient movement of users, and a specific embodiment thereof is described below.

[0223] First, the server retrieves user schedule information from the company's internal database. This includes information related to the next floor the user should move to, such as the location and time of scheduled meetings. The schedule information is pre-entered by the user according to their daily activities.

[0224] Next, imaging devices, or terminals, installed in elevator lobbies and corridors on each floor capture video at regular intervals and transmit this video data to a server. The server uses facial recognition technology to identify users from the captured video and, by linking this information with schedule information, predicts the user's next destination.

[0225] Based on this prediction, the server considers the current elevator layout and operating status to select the most suitable elevator. For the selected elevator, the server automatically sends a signal to press the appropriate "up / down button," allowing the user to board immediately upon arriving at the elevator hall.

[0226] The terminal, acting as part of the elevator's control panel, receives instructions from the server and automatically sets the destination floor after the user enters the elevator. Therefore, users do not need to press any buttons when entering the elevator, allowing them to reach their desired floor smoothly.

[0227] As a concrete example, consider the case where user A moves from the 5th floor to a conference room on the 12th floor. The server identifies the next destination as the 12th floor from user A's schedule information and predicts that user A will head to the 12th floor when user A is detected by a nearby camera. Based on this information, the server calls an elevator heading towards the 12th floor, and since the destination floor is already set to the 12th floor when the elevator arrives, user A can arrive at their destination immediately.

[0228] In this form of the invention, user movement can be optimized and time can be used efficiently. This will lead to further improvements in work efficiency in a smart office environment.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The server periodically retrieves schedule information for all users from the company's internal database. It collects data on upcoming meetings and appointments for each user and organizes it accordingly.

[0232] Step 2:

[0233] Imaging devices installed in corridors and elevator lobbies, which function as terminals, capture real-time video data at regular intervals and transmit it to a server. This video data captures the movements of users within the building.

[0234] Step 3:

[0235] The server uses facial recognition technology on the received video data to identify people in the video. The identified person information is then compared with previously collected schedule information and used to predict their movements.

[0236] Step 4:

[0237] The server compares the identified user's schedule information with the current time to predict the next floor the user should go to. This prediction serves as the basic data for elevator call operations.

[0238] Step 5:

[0239] The server checks the current location and operating status of the elevators and selects the most efficient elevator. The server then sends a signal to the selected elevator to press either the up or down button. This prepares the elevator to proceed to the user's desired floor.

[0240] Step 6:

[0241] The terminal (the control panel inside the elevator) automatically sets the predicted destination floor when a user enters the elevator, following instructions from the server. This automation eliminates the need for passengers to manually press buttons.

[0242] Step 7:

[0243] The user boards the elevator and confirms that it is set to the expected destination floor. The user can then exit the elevator without pressing any buttons and arrive at their destination smoothly. This entire system process ensures efficient travel.

[0244] (Example 1)

[0245] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0246] In office environments where efficient movement is essential, there is a need for means to reduce user effort and reach destinations quickly and accurately. Conventional elevator systems require users to manually select their destination floor, which is cumbersome and can reduce the overall efficiency of the process. Furthermore, during peak hours, multiple people boarding and alighting simultaneously makes optimal elevator use difficult.

[0247] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0248] In this invention, the server includes means for acquiring user activity information and estimating the next destination, means for acquiring photographic data from an installed recording device and utilizing identification technology, and means for controlling an elevator and automatically performing specific operations based on the estimated destination. This enables users to reach their destination level quickly and accurately without manual operation.

[0249] "Users" refers to individual people who travel using the elevator system.

[0250] "Activity information" refers to data about the user's plans and schedules, and is used to estimate their next destination.

[0251] "Destination" refers to the place the user plans to go to next.

[0252] A "recording device" refers to a machine or device installed to acquire image or video data.

[0253] "Shooting data" refers to image and video information acquired by a recording device.

[0254] "Identification technology" refers to technology that identifies specific individuals from video data using methods such as facial recognition.

[0255] A "lift" refers to a transport device used to move users between different floors, in other words, an elevator.

[0256] "Control" refers to adjusting the operation and position of elevators and escalators to properly manage the movement of users to their destinations.

[0257] This invention is an elevator system designed to support the efficient movement of users. This system combines multiple technical elements to enable users to reach their destination quickly and accurately. Specific embodiments are described below.

[0258] The server connects to the company's internal database to retrieve user activity information. This activity information includes the location and time of meetings and appointments, serving as basic data for estimating the next destination. Users store their information in this database by registering their schedules in the system in advance.

[0259] Recording devices installed on each floor and in elevator lobbies are used as terminals. These devices capture video at regular intervals and transmit the captured data to a server. The server uses identification technology on the video data, identifying users using software such as OpenCV and FaceNet. This makes it possible to link user identification information with activity information, allowing for accurate prediction of where users will go next.

[0260] Next, the server controls the optimal elevator based on the predicted destination, taking into account the current operating status and location information of the elevators within the system. Here, the server sends appropriate instruction signals to the elevators, ensuring that the destination floor is pre-set. This allows users to move smoothly to their destination floor as soon as they board the elevator.

[0261] As a concrete example, consider a scenario where a user moves from the 5th floor to a conference room on the 12th floor. The server estimates from the user's activity information that the conference on the 12th floor is the next destination and recognizes that the user is on the 5th floor. The server then selects the appropriate elevator and automatically adjusts it to head to the 12th floor. Once the user gets on the elevator, they can travel directly to the 12th floor.

[0262] A concrete example of a prompt message would be: "Please explain the mechanism that predicts the next destination based on the user's activity information, automatically arranges the most suitable elevator, and sets it to the destination floor."

[0263] This system contributes to the realization of a smart office environment by minimizing manual operation by users and providing efficiency and comfort.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The server accesses the company's internal database to retrieve user activity information. The input is schedule data from the database, and the output is information about each user's next destination. This information is processed by extracting the necessary data using SQL queries or similar methods.

[0267] Step 2:

[0268] The terminal captures video at regular intervals using recording devices installed in the elevator hall and on each floor. The input is raw video data captured by the camera, and the output is image data sent to the server. This image data is compressed and sent to the server via the network.

[0269] Step 3:

[0270] The server applies identification technology to the received image data to identify the user. The input is video data, and the output is the identification information of the identified user. This process uses OpenCV or FaceNet for facial recognition to extract the ID of a specific person.

[0271] Step 4:

[0272] The server matches user activity information based on identification information and predicts the next destination. The input is identification information and schedule information, and the output is the estimated next destination. At this stage, schedule matching is performed through data calculation to determine the most suitable next destination.

[0273] Step 5:

[0274] The server selects the most efficient elevator, taking into account its operational status and current location. The input is the elevator's current dispatch information and destination data, and the output is the ID of the selected elevator. An algorithm is used to evaluate the shortest route and operational efficiency to make the optimal selection.

[0275] Step 6:

[0276] The server sends appropriate instructions to the selected elevator. The input is the elevator ID and destination floor information, and the output is a control signal. The control signal instructs the elevator to automatically move to the target floor.

[0277] Step 7:

[0278] When a user boards an elevator, it automatically moves to a pre-set destination. No manual user input is required; the input is the boarding event, and the output is arrival at the destination floor. This allows users to reach their designated destination quickly and efficiently.

[0279] (Application Example 1)

[0280] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0281] In modern urban environments and commercial facilities, the efficient use of elevators is essential due to the large number of people moving around at once. However, existing systems struggle to accurately identify the user's position and select the most suitable elevator. Furthermore, requiring users to choose an elevator and operate buttons themselves leads to wasted time. The challenge lies in solving these problems and ensuring smoother user movement.

[0282] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes means for sending notifications to optimize the user's route, means for determining the user's location using the user's terminal location information, and means for accessing a cloud-based data management system to obtain schedule and operational information. This makes the user's route to their destination more efficient, reducing elevator waiting times and unnecessary movement, and enabling smoother travel.

[0284] "Notification for Optimizing User's Movement Route" refers to a message that notifies the terminal of optimal route and elevator information so that users can move efficiently within a building.

[0285] "Means for Identifying Location Using User Terminal's Location Information" refers to a technology that accurately identifies the current location of a user by utilizing the built-in GPS or beacon signals of a mobile information terminal such as a smartphone.

[0286] "Cloud-based Data Management System" refers to a distributed information processing system that collects and analyzes user data and elevator operation information via the Internet and provides necessary information promptly.

[0287] "Means for Identifying Users Using Facial Recognition Technology" refers to a technology that analyzes video data obtained using installed cameras to identify users.

[0288] "Means for Sending Notifications to Optimize Elevator Usage" refers to a technology that sends information about elevator availability and shortest routes to the user's terminal to enhance movement efficiency.

[0289] The system of this invention is constructed to integrate location information, facial recognition, and notification technology to support efficient movement of users. The server obtains the user's schedule information and the elevator operation status within the building in real time through a cloud-based data management system. Thereby, the optimal movement route for each user can be calculated and necessary notifications can be sent.

[0290] The terminal refers to a mobile information terminal such as a smartphone, which utilizes the built-in GPS to identify the current location of the user. Thereby, the optimal route information provided by the server can be received immediately and visually presented to the user.

[0291] This system incorporates facial recognition technology, allowing users to be identified by installed cameras. This information is then cross-referenced with a schedule by a server to ensure accurate notifications. Specifically, the server uses technology to optimize elevator usage and send notifications. This allows users to reach their destinations efficiently while minimizing elevator waiting times.

[0292] For example, if a user is a busy businessman with a series of meetings, the device's notification system will proactively call the next elevator he needs, helping him reach his destination in the shortest possible time. In this system, elevator availability is monitored by a cloud-based server, and the system is optimized based on the user's location and schedule.

[0293] An example of a prompt for a generated AI model is: "Design an application that allows users to efficiently move between multiple meeting rooms in a business building, and use facial recognition and scheduling data to indicate the optimal route." This prompt allows the model to devise an efficient mobility assistance system.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The user enables location services on a device such as a smartphone. The device uses its built-in GPS sensor to obtain the user's current location. This information is transmitted to the server via the device's communication module. The input is the device's location data, and the output is the transmission of the user's location information to the server.

[0297] Step 2:

[0298] The server uses a cloud-based data management system to obtain the user's schedule information and the operating status of the elevators in the building. The input is the request data based on the user ID, and the output is the schedule information related to the current time and the real-time elevator information.

[0299] Step 3:

[0300] The server applies the video data obtained from the installed cameras to a face recognition algorithm to identify the user. The input is the video data, and the output is the user's identification information. This identification information is compared with the schedule.

[0301] Step 4:

[0302] The server calculates the most efficient elevator route based on the user's identification information, schedule, and the operating status of the elevators. The input is the identified user information, schedule, and operating information, and the output is the specification of the optimal elevator route.

[0303] Step 5:

[0304] The server sends the calculated optimal elevator route as a notification to the user's terminal. The input is the optimized route information, and the output is the notification message to the user terminal.

[0305] Step 6:

[0306] The user goes to the designated elevator according to the received notification. The terminal displays the next floor or route as needed. The input is the notification content, and the output is the user's action.

[0307] Step 7:

[0308] Inside the elevator, the server automatically sets the destination of the elevator based on the pre-set destination floor. The input is the user's destination information, and the output is the instruction to the elevator control system.

[0309] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0310] This invention relates to a system that incorporates an emotion engine into an elevator system to provide flexible services that respond to the emotions of users. The embodiments thereof will be described in detail below.

[0311] First, in addition to conventional scheduling information and facial recognition functions, the server is equipped with an emotion engine for analyzing users' emotions in real time. This emotion engine analyzes video data acquired from terminals (imaging devices) installed in corridors and elevator lobbies, detecting users' facial expressions and voice tone to identify their emotional state.

[0312] Based on the results analyzed by the emotion engine, the server will instruct priority actions to reduce elevator waiting times if it determines that the user is stressed or in a hurry. Conversely, if the user is relaxed, normal operation will continue.

[0313] Furthermore, the server sends instructions to the control terminal inside the elevator to adjust the environment within the elevator. For example, if emotion analysis determines that a user is feeling tense, the lighting inside the elevator will be softened and relaxing music will be played to ensure the user is comfortable.

[0314] As a concrete example, consider a scenario where User B is rushing to a meeting. Based on the meeting schedule information, the server predicts User B's movement. The emotion engine analyzes the footage captured by the elevator hall camera and, if it detects that User B is slightly anxious, the server quickly optimizes the elevator and performs the action of pressing the "up button" earlier than usual.

[0315] When the elevator arrives and user B boards, music is played and the lighting is adjusted to provide a relaxing environment. This approach provides a service that is considerate of the user's feelings, enabling a more comfortable and efficient journey.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The server retrieves user schedule information from the building's database. This information includes the user's next destination and the time of their arrival.

[0319] Step 2:

[0320] The terminal (imaging device) captures video data of users in elevator lobbies and corridors and transmits it to the server in real time. This data is used for subsequent facial recognition and emotion analysis.

[0321] Step 3:

[0322] The server uses the transmitted video data to perform facial recognition and identify specific users. This information is then cross-referenced with the user's schedule information.

[0323] Step 4:

[0324] The server uses an emotion engine to analyze the user's emotional state from the transmitted video data. It determines whether the user is in a hurry, relaxed, or otherwise, based on changes in facial expressions and tone of voice.

[0325] Step 5:

[0326] The server adjusts the elevator's operation based on the results of emotion analysis. For example, if it determines that a user is in a hurry, the server will prioritize calling the elevator and issue instructions to reduce waiting time.

[0327] Step 6:

[0328] The terminal (the control unit inside the elevator) automatically sets the destination floor when a user boards, based on instructions from the server. It also adjusts the lighting and music based on the user's mood at the time of boarding, providing a comfortable environment. This operation enhances the user's elevator experience.

[0329] Step 7:

[0330] The user boards the elevator and travels to their pre-set destination floor. The comfortable environment and efficient travel allow the user to reach their destination without stress.

[0331] (Example 2)

[0332] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0333] Conventional vertical transport systems have struggled to provide flexible services that take into account users' emotions and schedules, making it difficult to achieve efficient and comfortable travel. In particular, they lacked the ability to accommodate time-sensitive travel situations and to create an environment that suits users' emotional needs.

[0334] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0335] In this invention, the server includes means for acquiring the user's scheduled time information and predicting the next destination, means for acquiring visual information from image acquisition devices installed along the route and performing personal recognition, and means for analyzing the user's emotional state and adjusting the operation of the vertical transport device. This makes it possible to provide a more comfortable and efficient travel service that is tailored to the user's schedule and emotions.

[0336] "Users" refer to individuals who utilize this system and are the recipients of services based on their scheduled time and emotional state.

[0337] "Scheduled time information" refers to information that shows the user's schedule and planned activities, and serves as basic data for predicting the next destination.

[0338] An "image acquisition device" is a device installed along a route or within a facility to acquire visual information and provide data for personal identification.

[0339] "Personal recognition" refers to technology that identifies users based on acquired visual information and analyzes their emotional state.

[0340] "Emotional state" refers to the state in which a user exhibits emotional responses, including emotions such as smiling, anger, anxiety, and relaxation.

[0341] A "vertical transport system" is a mechanical device used to move users between different floor levels, and is commonly known as an elevator.

[0342] "Means of adjusting the environment" refers to methods and technologies for adjusting lighting, music, and other elements inside vertical transport systems to provide users with a comfortable environment.

[0343] This system is an elevator system that provides flexible service tailored to the emotional state of the user. Specifically, a server and multiple terminals work together to collect and analyze user information, and adjust the elevator's operation and internal environment accordingly.

[0344] First, the terminal uses cameras installed in elevator lobbies and corridors to acquire video data of users in real time. This video data is sent to a server and analyzed by an emotion engine on the server. The emotion engine uses image processing libraries such as OpenCV and machine learning frameworks such as TensorFlow to analyze the user's facial expressions and tone of voice to identify their emotional state.

[0345] Furthermore, the server retrieves time schedule information from the user's smart device or IC card and compares it with the current schedule. Based on this information, it predicts the next destination and determines whether the user is in a hurry.

[0346] The server optimizes elevator operation and adjusts the environment inside the elevator according to the user's emotional state and schedule information. For example, if it determines that the user is stressed, it adjusts the elevator lighting to a warm color and plays relaxation music. This adjustment is intended to provide users with a comfortable and relaxing environment.

[0347] For example, if a user is rushing to a meeting, the server analyzes the video data from the camera using an emotion engine to detect that the user is anxious. The server then minimizes elevator waiting times and performs quick operations to help the user reach their destination smoothly.

[0348] An example of a prompt message would be, "How should the vertical transport system respond when the user is in a hurry?"

[0349] In this way, this system can provide more comfortable and efficient transportation services based on the user's emotional state.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The terminal uses cameras installed in elevator lobbies and corridors to acquire real-time video data of users. These cameras are configured to capture multiple users simultaneously and transmit the video data to a server. The input is video data, and the output is a digital video signal transmitted to the server.

[0353] Step 2:

[0354] The server receives video data transmitted from the terminal and begins analysis using an emotion engine. Here, OpenCV is used to detect the position and features of the face, and TensorFlow is used to identify the emotional state from facial expressions and voice tone. The input is the video signal from the terminal, and the output is data indicating the user's emotional state.

[0355] Step 3:

[0356] The server retrieves time schedule information from the user's smart device or IC card. This includes obtaining the latest schedule data. The input is time schedule data from the smart device, and the output is basic data for predicting the user's next destination.

[0357] Step 4:

[0358] The server combines the results of sentiment analysis with scheduled time information to evaluate the user's current state. It processes this data to determine the situation, such as whether the user is in a hurry or relaxed, and decides whether priority elevator service is needed. The inputs are sentiment state and scheduled time information, and the output is instruction data necessary for service optimization.

[0359] Step 5:

[0360] The server uses the generated instruction data to reset elevator waiting times and routes. In some cases, it issues instructions for emergency priority actions to optimize elevator operation. The input is instruction data, and the output is elevator control signals.

[0361] Step 6:

[0362] When a user enters an elevator, the server issues instructions to adjust the elevator's internal environment. This includes specific actions such as adjusting the color temperature of the lighting and setting appropriate background music. The input is the user's emotional state, and the output is the adjusted elevator environment.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0365] Conventional mobility systems struggle to provide flexible services that take into account the user's emotional state and schedule information, hindering user satisfaction and efficient travel. Furthermore, there is a challenge in that environmental adjustments to reduce user stress and anxiety are not automated.

[0366] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0367] In this invention, the server includes means for acquiring user schedule information and predicting the next destination, means for acquiring video data from an imaging device installed in the passageway and performing emotion analysis, means for calling a mobile device based on the predicted destination and automatically executing a specified operation, and means for adjusting the riding environment according to the emotional state of the user during the ride. This enables the provision of flexible services that respond to the user's emotions and the realization of efficient and comfortable travel.

[0368] A "user" refers to any human being who uses the system.

[0369] "Schedule information" refers to data that shows the user's schedule and activity plan.

[0370] "Next destination" refers to the place the user plans to go to next.

[0371] A "passageway" is a route used for movement inside or outside a building.

[0372] An "imaging device" is a device used to acquire images or videos.

[0373] "Video data" refers to the digital data of acquired images and videos.

[0374] "Emotional analysis" is the process of identifying an emotional state by analyzing characteristics such as facial expressions and voice.

[0375] "Mobility devices" refer to equipment used to transport people, such as elevators and escalators.

[0376] "Specified operation" refers to actions or procedures that the system has pre-configured.

[0377] "Riding environment" refers to environmental elements that users perceive within the vehicle, such as lighting and music.

[0378] To realize this invention, the system is configured as follows: The server uses a small computer such as a Raspberry Pi or AWS cloud services to acquire schedule information and predict the next destination. Terminals installed in the corridor are equipped with a camera (such as a Logitech C920) and a microphone, and acquire video and audio data through face recognition using OpenCV and speech recognition using the Google Cloud Speech-to-Text API. Using this, an emotion analysis library is used to identify the emotional state of the user from their facial expressions and voice.

[0379] Based on the emotion analysis, the server determines the user's emotional state and, based on that, summons a mobility device (e.g., an elevator or a household robot) and automatically performs the specified operation. Furthermore, the server adjusts the ride environment, playing music and adjusting the lighting to ensure the user's comfort.

[0380] As a concrete example, when a user returns home tired, the lighting in the vehicle is immediately adjusted to a relaxing level, and soothing music is played. This reduces the stress that users experience in their daily lives and provides a more comfortable environment.

[0381] An example of a prompt for a generative AI model is, "Please give me some ideas for programming an application that will enable a home robot to recognize the user's emotions and provide appropriate support." Using this prompt, insights can be gained regarding the development of robot applications with emotion recognition capabilities.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The server retrieves user schedule information from the database. It receives a user ID as input and retrieves the appointments and activity plans associated with that ID. As output, it obtains schedule information that serves as the basis for predicting the user's next destination. Based on this information, it calculates the predicted destination the user will take.

[0385] Step 2:

[0386] The terminal acquires video data using cameras installed in the corridor. It receives real-time video as input and performs face recognition using OpenCV. As output, it identifies the user's face and extracts feature vectors from the face image. These feature vectors are used later for sentiment analysis.

[0387] Step 3:

[0388] The device acquires audio data using a microphone. As input, it records the user's voice in real time and converts it to text using the Google Cloud Speech-to-Text API. As output, text data is generated, and its tone is used for sentiment analysis. This audio data also contributes to identifying emotional states.

[0389] Step 4:

[0390] The server uses an emotion analysis library to identify the user's emotional state from the facial features and audio text data obtained in steps 2 and 3. It receives facial features and voice tone as input and performs analysis using an emotion model. The identified emotional state is obtained as output. This allows for an understanding of the user's current mood and psychological state.

[0391] Step 5:

[0392] The server uses emotion analysis results and schedule information to summon and automate the operation of the mobile device. It selects the optimal mobile device using predicted destinations and emotional state information as input. The output includes summoning the mobile device and adjusting its waiting time. This operation reduces user stress and efficiently guides them to their destination.

[0393] Step 6:

[0394] When a user boards a mobile device, the server sends instructions to adjust the environment. It receives an identified emotional state as input and determines music and lighting settings accordingly. As output, the environment within the mobile device is automatically adjusted, providing a comfortable space for the user. This operation may further improve the user's mood.

[0395] 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.

[0396] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0397] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] 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.

[0401] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0402] 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.

[0403] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0404] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0405] 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.

[0406] 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.

[0407] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0408] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0409] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0410] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0411] This invention relates to an elevator system that supports the efficient movement of users, and a specific embodiment thereof is described below.

[0412] First, the server retrieves user schedule information from the company's internal database. This includes information related to the next floor the user should move to, such as the location and time of scheduled meetings. The schedule information is pre-entered by the user according to their daily activities.

[0413] Next, imaging devices, or terminals, installed in elevator lobbies and corridors on each floor capture video at regular intervals and transmit this video data to a server. The server uses facial recognition technology to identify users from the captured video and, by linking this information with schedule information, predicts the user's next destination.

[0414] Based on this prediction, the server considers the current elevator layout and operating status to select the most suitable elevator. For the selected elevator, the server automatically sends a signal to press the appropriate "up / down button," allowing the user to board immediately upon arriving at the elevator hall.

[0415] The terminal, acting as part of the elevator's control panel, receives instructions from the server and automatically sets the destination floor after the user enters the elevator. Therefore, users do not need to press any buttons when entering the elevator, allowing them to reach their desired floor smoothly.

[0416] As a concrete example, consider the case where user A moves from the 5th floor to a conference room on the 12th floor. The server identifies the next destination as the 12th floor from user A's schedule information and predicts that user A will head to the 12th floor when user A is detected by a nearby camera. Based on this information, the server calls an elevator heading towards the 12th floor, and since the destination floor is already set to the 12th floor when the elevator arrives, user A can arrive at their destination immediately.

[0417] In this form of the invention, user movement can be optimized and time can be used efficiently. This will lead to further improvements in work efficiency in a smart office environment.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The server periodically retrieves schedule information for all users from the company's internal database. It collects data on upcoming meetings and appointments for each user and organizes it accordingly.

[0421] Step 2:

[0422] Imaging devices installed in corridors and elevator lobbies, which function as terminals, capture real-time video data at regular intervals and transmit it to a server. This video data captures the movements of users within the building.

[0423] Step 3:

[0424] The server uses facial recognition technology on the received video data to identify people in the video. The identified person information is then compared with previously collected schedule information and used to predict their movements.

[0425] Step 4:

[0426] The server compares the identified user's schedule information with the current time to predict the next floor the user should go to. This prediction serves as the basic data for elevator call operations.

[0427] Step 5:

[0428] The server checks the current location and operating status of the elevators and selects the most efficient elevator. The server then sends a signal to the selected elevator to press either the up or down button. This prepares the elevator to proceed to the user's desired floor.

[0429] Step 6:

[0430] The terminal (the control panel inside the elevator) automatically sets the predicted destination floor when a user enters the elevator, following instructions from the server. This automation eliminates the need for passengers to manually press buttons.

[0431] Step 7:

[0432] The user boards the elevator and confirms that it is set to the expected destination floor. The user can then exit the elevator without pressing any buttons and arrive at their destination smoothly. This entire system process ensures efficient travel.

[0433] (Example 1)

[0434] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0435] In office environments where efficient movement is essential, there is a need for means to reduce user effort and reach destinations quickly and accurately. Conventional elevator systems require users to manually select their destination floor, which is cumbersome and can reduce the overall efficiency of the process. Furthermore, during peak hours, multiple people boarding and alighting simultaneously makes optimal elevator use difficult.

[0436] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0437] In this invention, the server includes means for acquiring user activity information and estimating the next destination, means for acquiring photographic data from an installed recording device and utilizing identification technology, and means for controlling an elevator and automatically performing specific operations based on the estimated destination. This enables users to reach their destination level quickly and accurately without manual operation.

[0438] "Users" refers to individual people who travel using the elevator system.

[0439] "Activity information" refers to data about the user's plans and schedules, and is used to estimate their next destination.

[0440] "Destination" refers to the place the user plans to go to next.

[0441] A "recording device" refers to a machine or device installed to acquire image or video data.

[0442] "Shooting data" refers to image and video information acquired by a recording device.

[0443] "Identification technology" refers to technology that identifies specific individuals from video data using methods such as facial recognition.

[0444] A "lift" refers to a transport device used to move users between different floors, in other words, an elevator.

[0445] "Control" refers to adjusting the operation and position of elevators and escalators to properly manage the movement of users to their destinations.

[0446] This invention is an elevator system designed to support the efficient movement of users. This system combines multiple technical elements to enable users to reach their destination quickly and accurately. Specific embodiments are described below.

[0447] The server connects to the company's internal database to retrieve user activity information. This activity information includes the location and time of meetings and appointments, serving as basic data for estimating the next destination. Users store their information in this database by registering their schedules in the system in advance.

[0448] Recording devices installed on each floor and in elevator lobbies are used as terminals. These devices capture video at regular intervals and transmit the captured data to a server. The server uses identification technology on the video data, identifying users using software such as OpenCV and FaceNet. This makes it possible to link user identification information with activity information, allowing for accurate prediction of where users will go next.

[0449] Next, the server controls the optimal elevator based on the predicted destination, taking into account the current operating status and location information of the elevators within the system. Here, the server sends appropriate instruction signals to the elevators, ensuring that the destination floor is pre-set. This allows users to move smoothly to their destination floor as soon as they board the elevator.

[0450] As a concrete example, consider a scenario where a user moves from the 5th floor to a conference room on the 12th floor. The server estimates from the user's activity information that the conference on the 12th floor is the next destination and recognizes that the user is on the 5th floor. The server then selects the appropriate elevator and automatically adjusts it to head to the 12th floor. Once the user gets on the elevator, they can travel directly to the 12th floor.

[0451] A concrete example of a prompt message would be: "Please explain the mechanism that predicts the next destination based on the user's activity information, automatically arranges the most suitable elevator, and sets it to the destination floor."

[0452] This system contributes to the realization of a smart office environment by minimizing manual operation by users and providing efficiency and comfort.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The server accesses the company's internal database to retrieve user activity information. The input is schedule data from the database, and the output is information about each user's next destination. This information is processed by extracting the necessary data using SQL queries or similar methods.

[0456] Step 2:

[0457] The terminal captures video at regular intervals using recording devices installed in the elevator hall and on each floor. The input is raw video data captured by the camera, and the output is image data sent to the server. This image data is compressed and sent to the server via the network.

[0458] Step 3:

[0459] The server applies identification technology to the received image data to identify the user. The input is video data, and the output is the identification information of the identified user. This process uses OpenCV or FaceNet for facial recognition to extract the ID of a specific person.

[0460] Step 4:

[0461] The server matches user activity information based on identification information and predicts the next destination. The input is identification information and schedule information, and the output is the estimated next destination. At this stage, schedule matching is performed through data calculation to determine the most suitable next destination.

[0462] Step 5:

[0463] The server selects the most efficient elevator, taking into account its operational status and current location. The input is the elevator's current dispatch information and destination data, and the output is the ID of the selected elevator. An algorithm is used to evaluate the shortest route and operational efficiency to make the optimal selection.

[0464] Step 6:

[0465] The server sends appropriate instructions to the selected elevator. The input is the elevator ID and destination floor information, and the output is a control signal. The control signal instructs the elevator to automatically move to the target floor.

[0466] Step 7:

[0467] When a user boards an elevator, it automatically moves to a pre-set destination. No manual user input is required; the input is the boarding event, and the output is arrival at the destination floor. This allows users to reach their designated destination quickly and efficiently.

[0468] (Application Example 1)

[0469] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0470] In modern urban environments and commercial facilities, the efficient use of elevators is essential due to the large number of people moving around at once. However, existing systems struggle to accurately identify the user's position and select the most suitable elevator. Furthermore, requiring users to choose an elevator and operate buttons themselves leads to wasted time. The challenge lies in solving these problems and ensuring smoother user movement.

[0471] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0472] In this invention, the server includes means for sending notifications to optimize the user's route, means for determining the user's location using the user's terminal location information, and means for accessing a cloud-based data management system to obtain schedule and operational information. This makes the user's route to their destination more efficient, reducing elevator waiting times and unnecessary movement, and enabling smoother travel.

[0473] "Notifications to optimize user routes" are messages that inform users of the optimal route and elevator information on their devices in order to help them move around the building efficiently.

[0474] "Means of determining location using location information of the user's terminal" refers to technologies that accurately determine the user's current location by utilizing the built-in GPS or beacon signals of mobile information terminals such as smartphones.

[0475] A "cloud-based data management system" is a distributed information processing system that collects and analyzes user data and elevator operation information via the internet, and provides necessary information quickly.

[0476] "Methods for identifying users using facial recognition technology" refers to technologies that analyze video data acquired using installed cameras to identify users.

[0477] "A means of sending notifications to optimize elevator use" refers to technology that sends information about elevator availability and the shortest route to the user's device, thereby making travel more efficient.

[0478] The system of this invention is built to support the efficient movement of users by integrating location information, facial recognition, and notification technologies. The server acquires users' schedule information and elevator operating status within buildings in real time through a cloud-based data management system. This allows the system to calculate the optimal route for each user and send necessary notifications.

[0479] The term "terminal" refers to a mobile information device such as a smartphone, which uses its built-in GPS to determine the user's current location. This allows the system to immediately receive optimal route information from the server and display it visually to the user.

[0480] This system incorporates facial recognition technology, allowing users to be identified by installed cameras. This information is then cross-referenced with a schedule by a server to ensure accurate notifications. Specifically, the server uses technology to optimize elevator usage and send notifications. This allows users to reach their destinations efficiently while minimizing elevator waiting times.

[0481] For example, if a user is a busy businessman with a series of meetings, the device's notification system will proactively call the next elevator he needs, helping him reach his destination in the shortest possible time. In this system, elevator availability is monitored by a cloud-based server, and the system is optimized based on the user's location and schedule.

[0482] An example of a prompt for a generated AI model is: "Design an application that allows users to efficiently move between multiple meeting rooms in a business building, and use facial recognition and scheduling data to indicate the optimal route." This prompt allows the model to devise an efficient mobility assistance system.

[0483] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0484] Step 1:

[0485] The user enables location services on a device such as a smartphone. The device uses its built-in GPS sensor to obtain the user's current location. This information is transmitted to the server via the device's communication module. The input is the device's location data, and the output is the transmission of the user's location information to the server.

[0486] Step 2:

[0487] The server uses a cloud-based data management system to retrieve user schedule information and elevator operating status within the building. Input is request data based on the user ID, and output is schedule information related to the current time and real-time elevator information.

[0488] Step 3:

[0489] The server uses a facial recognition algorithm to process video data acquired from installed cameras to identify users. The input is video data, and the output is user identification information. This identification information is then cross-referenced with the schedule.

[0490] Step 4:

[0491] The server calculates the most efficient elevator route based on user identification information, schedule, and elevator operating status. The inputs are identified user information, schedule, and operating status, and the output is the specified optimal elevator route.

[0492] Step 5:

[0493] The server sends the calculated optimal elevator route as a notification to the user's terminal. The input is the optimized route information, and the output is the notification message sent to the user's terminal.

[0494] Step 6:

[0495] The user follows the received notification and heads to the designated elevator. The terminal displays the next floor and directions as needed. The input is the notification content, and the output is the user's action.

[0496] Step 7:

[0497] Inside the elevator, the server automatically sets the elevator's destination based on a pre-configured destination floor. The input is the user's destination information, and the output is an instruction to the elevator control system.

[0498] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0499] This invention relates to a system that incorporates an emotion engine into an elevator system to provide flexible services that respond to the emotions of users. The embodiments thereof will be described in detail below.

[0500] First, in addition to conventional scheduling information and facial recognition functions, the server is equipped with an emotion engine for analyzing users' emotions in real time. This emotion engine analyzes video data acquired from terminals (imaging devices) installed in corridors and elevator lobbies, detecting users' facial expressions and voice tone to identify their emotional state.

[0501] Based on the results analyzed by the emotion engine, the server will instruct priority actions to reduce elevator waiting times if it determines that the user is stressed or in a hurry. Conversely, if the user is relaxed, normal operation will continue.

[0502] Furthermore, the server sends instructions to the control terminal inside the elevator to adjust the environment within the elevator. For example, if emotion analysis determines that a user is feeling tense, the lighting inside the elevator will be softened and relaxing music will be played to ensure the user is comfortable.

[0503] As a concrete example, consider a scenario where User B is rushing to a meeting. Based on the meeting schedule information, the server predicts User B's movement. The emotion engine analyzes the footage captured by the elevator hall camera and, if it detects that User B is slightly anxious, the server quickly optimizes the elevator and performs the action of pressing the "up button" earlier than usual.

[0504] When the elevator arrives and user B boards, music is played and the lighting is adjusted to provide a relaxing environment. This approach provides a service that is considerate of the user's feelings, enabling a more comfortable and efficient journey.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] The server retrieves user schedule information from the building's database. This information includes the user's next destination and the time of their arrival.

[0508] Step 2:

[0509] The terminal (imaging device) captures video data of users in elevator lobbies and corridors and transmits it to the server in real time. This data is used for subsequent facial recognition and emotion analysis.

[0510] Step 3:

[0511] The server uses the transmitted video data to perform facial recognition and identify specific users. This information is then cross-referenced with the user's schedule information.

[0512] Step 4:

[0513] The server uses an emotion engine to analyze the user's emotional state from the transmitted video data. It determines whether the user is in a hurry, relaxed, or otherwise, based on changes in facial expressions and tone of voice.

[0514] Step 5:

[0515] The server adjusts the elevator's operation based on the results of emotion analysis. For example, if it determines that a user is in a hurry, the server will prioritize calling the elevator and issue instructions to reduce waiting time.

[0516] Step 6:

[0517] The terminal (the control unit inside the elevator) automatically sets the destination floor when a user boards, based on instructions from the server. It also adjusts the lighting and music based on the user's mood at the time of boarding, providing a comfortable environment. This operation enhances the user's elevator experience.

[0518] Step 7:

[0519] The user boards the elevator and travels to their pre-set destination floor. The comfortable environment and efficient travel allow the user to reach their destination without stress.

[0520] (Example 2)

[0521] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0522] Conventional vertical transport systems have struggled to provide flexible services that take into account users' emotions and schedules, making it difficult to achieve efficient and comfortable travel. In particular, they lacked the ability to accommodate time-sensitive travel situations and to create an environment that suits users' emotional needs.

[0523] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0524] In this invention, the server includes means for acquiring the user's scheduled time information and predicting the next destination, means for acquiring visual information from image acquisition devices installed along the route and performing personal recognition, and means for analyzing the user's emotional state and adjusting the operation of the vertical transport device. This makes it possible to provide a more comfortable and efficient travel service that is tailored to the user's schedule and emotions.

[0525] "Users" refer to individuals who utilize this system and are the recipients of services based on their scheduled time and emotional state.

[0526] "Scheduled time information" refers to information that shows the user's schedule and planned activities, and serves as basic data for predicting the next destination.

[0527] An "image acquisition device" is a device installed along a route or within a facility to acquire visual information and provide data for personal identification.

[0528] "Personal recognition" refers to technology that identifies users based on acquired visual information and analyzes their emotional state.

[0529] "Emotional state" refers to the state in which a user exhibits emotional responses, including emotions such as smiling, anger, anxiety, and relaxation.

[0530] A "vertical transport system" is a mechanical device used to move users between different floor levels, and is commonly known as an elevator.

[0531] "Means of adjusting the environment" refers to methods and technologies for adjusting lighting, music, and other elements inside vertical transport systems to provide users with a comfortable environment.

[0532] This system is an elevator system that provides flexible service tailored to the emotional state of the user. Specifically, a server and multiple terminals work together to collect and analyze user information, and adjust the elevator's operation and internal environment accordingly.

[0533] First, the terminal uses cameras installed in elevator lobbies and corridors to acquire video data of users in real time. This video data is sent to a server and analyzed by an emotion engine on the server. The emotion engine uses image processing libraries such as OpenCV and machine learning frameworks such as TensorFlow to analyze the user's facial expressions and tone of voice to identify their emotional state.

[0534] Furthermore, the server retrieves time schedule information from the user's smart device or IC card and compares it with the current schedule. Based on this information, it predicts the next destination and determines whether the user is in a hurry.

[0535] The server optimizes elevator operation and adjusts the environment inside the elevator according to the user's emotional state and schedule information. For example, if it determines that the user is stressed, it adjusts the elevator lighting to a warm color and plays relaxation music. This adjustment is intended to provide users with a comfortable and relaxing environment.

[0536] For example, if a user is rushing to a meeting, the server analyzes the video data from the camera using an emotion engine to detect that the user is anxious. The server then minimizes elevator waiting times and performs quick operations to help the user reach their destination smoothly.

[0537] An example of a prompt message would be, "How should the vertical transport system respond when the user is in a hurry?"

[0538] In this way, this system can provide more comfortable and efficient transportation services based on the user's emotional state.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The terminal uses cameras installed in elevator lobbies and corridors to acquire real-time video data of users. These cameras are configured to capture multiple users simultaneously and transmit the video data to a server. The input is video data, and the output is a digital video signal transmitted to the server.

[0542] Step 2:

[0543] The server receives video data transmitted from the terminal and begins analysis using an emotion engine. Here, OpenCV is used to detect the position and features of the face, and TensorFlow is used to identify the emotional state from facial expressions and voice tone. The input is the video signal from the terminal, and the output is data indicating the user's emotional state.

[0544] Step 3:

[0545] The server retrieves time schedule information from the user's smart device or IC card. This includes obtaining the latest schedule data. The input is time schedule data from the smart device, and the output is basic data for predicting the user's next destination.

[0546] Step 4:

[0547] The server combines the results of sentiment analysis with scheduled time information to evaluate the user's current state. It processes this data to determine the situation, such as whether the user is in a hurry or relaxed, and decides whether priority elevator service is needed. The inputs are sentiment state and scheduled time information, and the output is instruction data necessary for service optimization.

[0548] Step 5:

[0549] The server uses the generated instruction data to reset elevator waiting times and routes. In some cases, it issues instructions for emergency priority actions to optimize elevator operation. The input is instruction data, and the output is elevator control signals.

[0550] Step 6:

[0551] When a user enters an elevator, the server issues instructions to adjust the elevator's internal environment. This includes specific actions such as adjusting the color temperature of the lighting and setting appropriate background music. The input is the user's emotional state, and the output is the adjusted elevator environment.

[0552] (Application Example 2)

[0553] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0554] Conventional mobility systems struggle to provide flexible services that take into account the user's emotional state and schedule information, hindering user satisfaction and efficient travel. Furthermore, there is a challenge in that environmental adjustments to reduce user stress and anxiety are not automated.

[0555] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0556] In this invention, the server includes means for acquiring user schedule information and predicting the next destination, means for acquiring video data from an imaging device installed in the passageway and performing emotion analysis, means for calling a mobile device based on the predicted destination and automatically executing a specified operation, and means for adjusting the riding environment according to the emotional state of the user during the ride. This enables the provision of flexible services that respond to the user's emotions and the realization of efficient and comfortable travel.

[0557] A "user" refers to any human being who uses the system.

[0558] "Schedule information" refers to data that shows the user's schedule and activity plan.

[0559] "Next destination" refers to the place the user plans to go to next.

[0560] A "passageway" is a route used for movement inside or outside a building.

[0561] An "imaging device" is a device used to acquire images or videos.

[0562] "Video data" refers to the digital data of acquired images and videos.

[0563] "Emotional analysis" is the process of identifying an emotional state by analyzing characteristics such as facial expressions and voice.

[0564] "Mobility devices" refer to equipment used to transport people, such as elevators and escalators.

[0565] "Specified operation" refers to actions or procedures that the system has pre-configured.

[0566] "Riding environment" refers to environmental elements that users perceive within the vehicle, such as lighting and music.

[0567] To realize this invention, the system is configured as follows: The server uses a small computer such as a Raspberry Pi or AWS cloud services to acquire schedule information and predict the next destination. Terminals installed in the corridor are equipped with a camera (such as a Logitech C920) and a microphone, and acquire video and audio data through face recognition using OpenCV and speech recognition using the Google Cloud Speech-to-Text API. Using this, an emotion analysis library is used to identify the emotional state of the user from their facial expressions and voice.

[0568] Based on the emotion analysis, the server determines the user's emotional state and, based on that, summons a mobility device (e.g., an elevator or a household robot) and automatically performs the specified operation. Furthermore, the server adjusts the ride environment, playing music and adjusting the lighting to ensure the user's comfort.

[0569] As a concrete example, when a user returns home tired, the lighting in the vehicle is immediately adjusted to a relaxing level, and soothing music is played. This reduces the stress that users experience in their daily lives and provides a more comfortable environment.

[0570] An example of a prompt for a generative AI model is, "Please give me some ideas for programming an application that will enable a home robot to recognize the user's emotions and provide appropriate support." Using this prompt, insights can be gained regarding the development of robot applications with emotion recognition capabilities.

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The server retrieves user schedule information from the database. It receives a user ID as input and retrieves the appointments and activity plans associated with that ID. As output, it obtains schedule information that serves as the basis for predicting the user's next destination. Based on this information, it calculates the predicted destination the user will take.

[0574] Step 2:

[0575] The terminal acquires video data using cameras installed in the corridor. It receives real-time video as input and performs face recognition using OpenCV. As output, it identifies the user's face and extracts feature vectors from the face image. These feature vectors are used later for sentiment analysis.

[0576] Step 3:

[0577] The device acquires audio data using a microphone. As input, it records the user's voice in real time and converts it to text using the Google Cloud Speech-to-Text API. As output, text data is generated, and its tone is used for sentiment analysis. This audio data also contributes to identifying emotional states.

[0578] Step 4:

[0579] The server uses an emotion analysis library to identify the user's emotional state from the facial features and audio text data obtained in steps 2 and 3. It receives facial features and voice tone as input and performs analysis using an emotion model. The identified emotional state is obtained as output. This allows for an understanding of the user's current mood and psychological state.

[0580] Step 5:

[0581] The server uses emotion analysis results and schedule information to summon and automate the operation of the mobile device. It selects the optimal mobile device using predicted destinations and emotional state information as input. The output includes summoning the mobile device and adjusting its waiting time. This operation reduces user stress and efficiently guides them to their destination.

[0582] Step 6:

[0583] When a user boards a mobile device, the server sends instructions to adjust the environment. It receives an identified emotional state as input and determines music and lighting settings accordingly. As output, the environment within the mobile device is automatically adjusted, providing a comfortable space for the user. This operation may further improve the user's mood.

[0584] 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.

[0585] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0586] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] 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.

[0590] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0591] 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.

[0592] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0593] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0594] 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.

[0595] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0596] 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.

[0597] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0598] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0599] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0600] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0601] This invention relates to an elevator system that supports the efficient movement of users, and a specific embodiment thereof is described below.

[0602] First, the server retrieves user schedule information from the company's internal database. This includes information related to the next floor the user should move to, such as the location and time of scheduled meetings. The schedule information is pre-entered by the user according to their daily activities.

[0603] Next, imaging devices, or terminals, installed in elevator lobbies and corridors on each floor capture video at regular intervals and transmit this video data to a server. The server uses facial recognition technology to identify users from the captured video and, by linking this information with schedule information, predicts the user's next destination.

[0604] Based on this prediction, the server considers the current elevator layout and operating status to select the most suitable elevator. For the selected elevator, the server automatically sends a signal to press the appropriate "up / down button," allowing the user to board immediately upon arriving at the elevator hall.

[0605] The terminal, acting as part of the elevator's control panel, receives instructions from the server and automatically sets the destination floor after the user enters the elevator. Therefore, users do not need to press any buttons when entering the elevator, allowing them to reach their desired floor smoothly.

[0606] As a concrete example, consider the case where user A moves from the 5th floor to a conference room on the 12th floor. The server identifies the next destination as the 12th floor from user A's schedule information and predicts that user A will head to the 12th floor when user A is detected by a nearby camera. Based on this information, the server calls an elevator heading towards the 12th floor, and since the destination floor is already set to the 12th floor when the elevator arrives, user A can arrive at their destination immediately.

[0607] In this form of the invention, user movement can be optimized and time can be used efficiently. This will lead to further improvements in work efficiency in a smart office environment.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The server periodically retrieves schedule information for all users from the company's internal database. It collects data on upcoming meetings and appointments for each user and organizes it accordingly.

[0611] Step 2:

[0612] Imaging devices installed in corridors and elevator lobbies, which function as terminals, capture real-time video data at regular intervals and transmit it to a server. This video data captures the movements of users within the building.

[0613] Step 3:

[0614] The server uses facial recognition technology on the received video data to identify people in the video. The identified person information is then compared with previously collected schedule information and used to predict their movements.

[0615] Step 4:

[0616] The server compares the identified user's schedule information with the current time to predict the next floor the user should go to. This prediction serves as the basic data for elevator call operations.

[0617] Step 5:

[0618] The server checks the current location and operating status of the elevators and selects the most efficient elevator. The server then sends a signal to the selected elevator to press either the up or down button. This prepares the elevator to proceed to the user's desired floor.

[0619] Step 6:

[0620] The terminal (the control panel inside the elevator) automatically sets the predicted destination floor when a user enters the elevator, following instructions from the server. This automation eliminates the need for passengers to manually press buttons.

[0621] Step 7:

[0622] The user boards the elevator and confirms that it is set to the expected destination floor. The user can then exit the elevator without pressing any buttons and arrive at their destination smoothly. This entire system process ensures efficient travel.

[0623] (Example 1)

[0624] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] In office environments where efficient movement is essential, there is a need for means to reduce user effort and reach destinations quickly and accurately. Conventional elevator systems require users to manually select their destination floor, which is cumbersome and can reduce the overall efficiency of the process. Furthermore, during peak hours, multiple people boarding and alighting simultaneously makes optimal elevator use difficult.

[0626] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0627] In this invention, the server includes means for acquiring user activity information and estimating the next destination, means for acquiring photographic data from an installed recording device and utilizing identification technology, and means for controlling an elevator and automatically performing specific operations based on the estimated destination. This enables users to reach their destination level quickly and accurately without manual operation.

[0628] "Users" refers to individual people who travel using the elevator system.

[0629] "Activity information" refers to data about the user's plans and schedules, and is used to estimate their next destination.

[0630] "Destination" refers to the place the user plans to go to next.

[0631] A "recording device" refers to a machine or device installed to acquire image or video data.

[0632] "Shooting data" refers to image and video information acquired by a recording device.

[0633] "Identification technology" refers to technology that identifies specific individuals from video data using methods such as facial recognition.

[0634] A "lift" refers to a transport device used to move users between different floors, in other words, an elevator.

[0635] "Control" refers to adjusting the operation and position of elevators and escalators to properly manage the movement of users to their destinations.

[0636] This invention is an elevator system designed to support the efficient movement of users. This system combines multiple technical elements to enable users to reach their destination quickly and accurately. Specific embodiments are described below.

[0637] The server connects to the company's internal database to retrieve user activity information. This activity information includes the location and time of meetings and appointments, serving as basic data for estimating the next destination. Users store their information in this database by registering their schedules in the system in advance.

[0638] Recording devices installed on each floor and in elevator lobbies are used as terminals. These devices capture video at regular intervals and transmit the captured data to a server. The server uses identification technology on the video data, identifying users using software such as OpenCV and FaceNet. This makes it possible to link user identification information with activity information, allowing for accurate prediction of where users will go next.

[0639] Next, the server controls the optimal elevator based on the predicted destination, taking into account the current operating status and location information of the elevators within the system. Here, the server sends appropriate instruction signals to the elevators, ensuring that the destination floor is pre-set. This allows users to move smoothly to their destination floor as soon as they board the elevator.

[0640] As a concrete example, consider a scenario where a user moves from the 5th floor to a conference room on the 12th floor. The server estimates from the user's activity information that the conference on the 12th floor is the next destination and recognizes that the user is on the 5th floor. The server then selects the appropriate elevator and automatically adjusts it to head to the 12th floor. Once the user gets on the elevator, they can travel directly to the 12th floor.

[0641] A concrete example of a prompt message would be: "Please explain the mechanism that predicts the next destination based on the user's activity information, automatically arranges the most suitable elevator, and sets it to the destination floor."

[0642] This system contributes to the realization of a smart office environment by minimizing manual operation by users and providing efficiency and comfort.

[0643] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0644] Step 1:

[0645] The server accesses the company's internal database to retrieve user activity information. The input is schedule data from the database, and the output is information about each user's next destination. This information is processed by extracting the necessary data using SQL queries or similar methods.

[0646] Step 2:

[0647] The terminal captures video at regular intervals using recording devices installed in the elevator hall and on each floor. The input is raw video data captured by the camera, and the output is image data sent to the server. This image data is compressed and sent to the server via the network.

[0648] Step 3:

[0649] The server applies identification technology to the received image data to identify the user. The input is video data, and the output is the identification information of the identified user. This process uses OpenCV or FaceNet for facial recognition to extract the ID of a specific person.

[0650] Step 4:

[0651] The server matches user activity information based on identification information and predicts the next destination. The input is identification information and schedule information, and the output is the estimated next destination. At this stage, schedule matching is performed through data calculation to determine the most suitable next destination.

[0652] Step 5:

[0653] The server selects the most efficient elevator, taking into account its operational status and current location. The input is the elevator's current dispatch information and destination data, and the output is the ID of the selected elevator. An algorithm is used to evaluate the shortest route and operational efficiency to make the optimal selection.

[0654] Step 6:

[0655] The server sends appropriate instructions to the selected elevator. The input is the elevator ID and destination floor information, and the output is a control signal. The control signal instructs the elevator to automatically move to the target floor.

[0656] Step 7:

[0657] When a user boards an elevator, it automatically moves to a pre-set destination. No manual user input is required; the input is the boarding event, and the output is arrival at the destination floor. This allows users to reach their designated destination quickly and efficiently.

[0658] (Application Example 1)

[0659] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] In modern urban environments and commercial facilities, the efficient use of elevators is essential due to the large number of people moving around at once. However, existing systems struggle to accurately identify the user's position and select the most suitable elevator. Furthermore, requiring users to choose an elevator and operate buttons themselves leads to wasted time. The challenge lies in solving these problems and ensuring smoother user movement.

[0661] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0662] In this invention, the server includes means for sending notifications to optimize the user's route, means for determining the user's location using the user's terminal location information, and means for accessing a cloud-based data management system to obtain schedule and operational information. This makes the user's route to their destination more efficient, reducing elevator waiting times and unnecessary movement, and enabling smoother travel.

[0663] "Notifications to optimize user routes" are messages that inform users of the optimal route and elevator information on their devices in order to help them move around the building efficiently.

[0664] "Means of determining location using location information of the user's terminal" refers to technologies that accurately determine the user's current location by utilizing the built-in GPS or beacon signals of mobile information terminals such as smartphones.

[0665] A "cloud-based data management system" is a distributed information processing system that collects and analyzes user data and elevator operation information via the internet, and provides necessary information quickly.

[0666] "Methods for identifying users using facial recognition technology" refers to technologies that analyze video data acquired using installed cameras to identify users.

[0667] "A means of sending notifications to optimize elevator use" refers to technology that sends information about elevator availability and the shortest route to the user's device, thereby making travel more efficient.

[0668] The system of this invention is built to support the efficient movement of users by integrating location information, facial recognition, and notification technologies. The server acquires users' schedule information and elevator operating status within buildings in real time through a cloud-based data management system. This allows the system to calculate the optimal route for each user and send necessary notifications.

[0669] The term "terminal" refers to a mobile information device such as a smartphone, which uses its built-in GPS to determine the user's current location. This allows the system to immediately receive optimal route information from the server and display it visually to the user.

[0670] This system incorporates facial recognition technology, allowing users to be identified by installed cameras. This information is then cross-referenced with a schedule by a server to ensure accurate notifications. Specifically, the server uses technology to optimize elevator usage and send notifications. This allows users to reach their destinations efficiently while minimizing elevator waiting times.

[0671] For example, if a user is a busy businessman with a series of meetings, the device's notification system will proactively call the next elevator he needs, helping him reach his destination in the shortest possible time. In this system, elevator availability is monitored by a cloud-based server, and the system is optimized based on the user's location and schedule.

[0672] An example of a prompt for a generated AI model is: "Design an application that allows users to efficiently move between multiple meeting rooms in a business building, and use facial recognition and scheduling data to indicate the optimal route." This prompt allows the model to devise an efficient mobility assistance system.

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] The user enables location services on a device such as a smartphone. The device uses its built-in GPS sensor to obtain the user's current location. This information is transmitted to the server via the device's communication module. The input is the device's location data, and the output is the transmission of the user's location information to the server.

[0676] Step 2:

[0677] The server uses a cloud-based data management system to retrieve user schedule information and elevator operating status within the building. Input is request data based on the user ID, and output is schedule information related to the current time and real-time elevator information.

[0678] Step 3:

[0679] The server uses a facial recognition algorithm to process video data acquired from installed cameras to identify users. The input is video data, and the output is user identification information. This identification information is then cross-referenced with the schedule.

[0680] Step 4:

[0681] The server calculates the most efficient elevator route based on user identification information, schedule, and elevator operating status. The inputs are identified user information, schedule, and operating status, and the output is the specified optimal elevator route.

[0682] Step 5:

[0683] The server sends the calculated optimal elevator route as a notification to the user's terminal. The input is the optimized route information, and the output is the notification message sent to the user's terminal.

[0684] Step 6:

[0685] The user follows the received notification and heads to the designated elevator. The terminal displays the next floor and directions as needed. The input is the notification content, and the output is the user's action.

[0686] Step 7:

[0687] Inside the elevator, the server automatically sets the elevator's destination based on a pre-configured destination floor. The input is the user's destination information, and the output is an instruction to the elevator control system.

[0688] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0689] This invention relates to a system that incorporates an emotion engine into an elevator system to provide flexible services that respond to the emotions of users. The embodiments thereof will be described in detail below.

[0690] First, in addition to conventional scheduling information and facial recognition functions, the server is equipped with an emotion engine for analyzing users' emotions in real time. This emotion engine analyzes video data acquired from terminals (imaging devices) installed in corridors and elevator lobbies, detecting users' facial expressions and voice tone to identify their emotional state.

[0691] Based on the results analyzed by the emotion engine, the server will instruct priority actions to reduce elevator waiting times if it determines that the user is stressed or in a hurry. Conversely, if the user is relaxed, normal operation will continue.

[0692] Furthermore, the server sends instructions to the control terminal inside the elevator to adjust the environment within the elevator. For example, if emotion analysis determines that a user is feeling tense, the lighting inside the elevator will be softened and relaxing music will be played to ensure the user is comfortable.

[0693] As a concrete example, consider a scenario where User B is rushing to a meeting. Based on the meeting schedule information, the server predicts User B's movement. The emotion engine analyzes the footage captured by the elevator hall camera and, if it detects that User B is slightly anxious, the server quickly optimizes the elevator and performs the action of pressing the "up button" earlier than usual.

[0694] When the elevator arrives and user B boards, music is played and the lighting is adjusted to provide a relaxing environment. This approach provides a service that is considerate of the user's feelings, enabling a more comfortable and efficient journey.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The server retrieves user schedule information from the building's database. This information includes the user's next destination and the time of their arrival.

[0698] Step 2:

[0699] The terminal (imaging device) captures video data of users in elevator lobbies and corridors and transmits it to the server in real time. This data is used for subsequent facial recognition and emotion analysis.

[0700] Step 3:

[0701] The server uses the transmitted video data to perform facial recognition and identify specific users. This information is then cross-referenced with the user's schedule information.

[0702] Step 4:

[0703] The server uses an emotion engine to analyze the user's emotional state from the transmitted video data. It determines whether the user is in a hurry, relaxed, or otherwise, based on changes in facial expressions and tone of voice.

[0704] Step 5:

[0705] The server adjusts the elevator's operation based on the results of emotion analysis. For example, if it determines that a user is in a hurry, the server will prioritize calling the elevator and issue instructions to reduce waiting time.

[0706] Step 6:

[0707] The terminal (the control unit inside the elevator) automatically sets the destination floor when a user boards, based on instructions from the server. It also adjusts the lighting and music based on the user's mood at the time of boarding, providing a comfortable environment. This operation enhances the user's elevator experience.

[0708] Step 7:

[0709] The user boards the elevator and travels to their pre-set destination floor. The comfortable environment and efficient travel allow the user to reach their destination without stress.

[0710] (Example 2)

[0711] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] Conventional vertical transport systems have struggled to provide flexible services that take into account users' emotions and schedules, making it difficult to achieve efficient and comfortable travel. In particular, they lacked the ability to accommodate time-sensitive travel situations and to create an environment that suits users' emotional needs.

[0713] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0714] In this invention, the server includes means for acquiring the user's scheduled time information and predicting the next destination, means for acquiring visual information from image acquisition devices installed along the route and performing personal recognition, and means for analyzing the user's emotional state and adjusting the operation of the vertical transport device. This makes it possible to provide a more comfortable and efficient travel service that is tailored to the user's schedule and emotions.

[0715] "Users" refer to individuals who utilize this system and are the recipients of services based on their scheduled time and emotional state.

[0716] "Scheduled time information" refers to information that shows the user's schedule and planned activities, and serves as basic data for predicting the next destination.

[0717] An "image acquisition device" is a device installed along a route or within a facility to acquire visual information and provide data for personal identification.

[0718] "Personal recognition" refers to technology that identifies users based on acquired visual information and analyzes their emotional state.

[0719] "Emotional state" refers to the state in which a user exhibits emotional responses, including emotions such as smiling, anger, anxiety, and relaxation.

[0720] A "vertical transport system" is a mechanical device used to move users between different floor levels, and is commonly known as an elevator.

[0721] "Means of adjusting the environment" refers to methods and technologies for adjusting lighting, music, and other elements inside vertical transport systems to provide users with a comfortable environment.

[0722] This system is an elevator system that provides flexible service tailored to the emotional state of the user. Specifically, a server and multiple terminals work together to collect and analyze user information, and adjust the elevator's operation and internal environment accordingly.

[0723] First, the terminal uses cameras installed in elevator lobbies and corridors to acquire video data of users in real time. This video data is sent to a server and analyzed by an emotion engine on the server. The emotion engine uses image processing libraries such as OpenCV and machine learning frameworks such as TensorFlow to analyze the user's facial expressions and tone of voice to identify their emotional state.

[0724] Furthermore, the server retrieves time schedule information from the user's smart device or IC card and compares it with the current schedule. Based on this information, it predicts the next destination and determines whether the user is in a hurry.

[0725] The server optimizes elevator operation and adjusts the environment inside the elevator according to the user's emotional state and schedule information. For example, if it determines that the user is stressed, it adjusts the elevator lighting to a warm color and plays relaxation music. This adjustment is intended to provide users with a comfortable and relaxing environment.

[0726] For example, if a user is rushing to a meeting, the server analyzes the video data from the camera using an emotion engine to detect that the user is anxious. The server then minimizes elevator waiting times and performs quick operations to help the user reach their destination smoothly.

[0727] An example of a prompt message would be, "How should the vertical transport system respond when the user is in a hurry?"

[0728] In this way, this system can provide more comfortable and efficient transportation services based on the user's emotional state.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] The terminal uses cameras installed in elevator lobbies and corridors to acquire real-time video data of users. These cameras are configured to capture multiple users simultaneously and transmit the video data to a server. The input is video data, and the output is a digital video signal transmitted to the server.

[0732] Step 2:

[0733] The server receives video data transmitted from the terminal and begins analysis using an emotion engine. Here, OpenCV is used to detect the position and features of the face, and TensorFlow is used to identify the emotional state from facial expressions and voice tone. The input is the video signal from the terminal, and the output is data indicating the user's emotional state.

[0734] Step 3:

[0735] The server retrieves time schedule information from the user's smart device or IC card. This includes obtaining the latest schedule data. The input is time schedule data from the smart device, and the output is basic data for predicting the user's next destination.

[0736] Step 4:

[0737] The server combines the results of sentiment analysis with scheduled time information to evaluate the user's current state. It processes this data to determine the situation, such as whether the user is in a hurry or relaxed, and decides whether priority elevator service is needed. The inputs are sentiment state and scheduled time information, and the output is instruction data necessary for service optimization.

[0738] Step 5:

[0739] The server uses the generated instruction data to reset elevator waiting times and routes. In some cases, it issues instructions for emergency priority actions to optimize elevator operation. The input is instruction data, and the output is elevator control signals.

[0740] Step 6:

[0741] When a user enters an elevator, the server issues instructions to adjust the elevator's internal environment. This includes specific actions such as adjusting the color temperature of the lighting and setting appropriate background music. The input is the user's emotional state, and the output is the adjusted elevator environment.

[0742] (Application Example 2)

[0743] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0744] Conventional mobility systems struggle to provide flexible services that take into account the user's emotional state and schedule information, hindering user satisfaction and efficient travel. Furthermore, there is a challenge in that environmental adjustments to reduce user stress and anxiety are not automated.

[0745] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0746] In this invention, the server includes means for acquiring user schedule information and predicting the next destination, means for acquiring video data from an imaging device installed in the passageway and performing emotion analysis, means for calling a mobile device based on the predicted destination and automatically executing a specified operation, and means for adjusting the riding environment according to the emotional state of the user during the ride. This enables the provision of flexible services that respond to the user's emotions and the realization of efficient and comfortable travel.

[0747] A "user" refers to any human being who uses the system.

[0748] "Schedule information" refers to data that shows the user's schedule and activity plan.

[0749] "Next destination" refers to the place the user plans to go to next.

[0750] A "passageway" is a route used for movement inside or outside a building.

[0751] An "imaging device" is a device used to acquire images or videos.

[0752] "Video data" refers to the digital data of acquired images and videos.

[0753] "Emotional analysis" is the process of identifying an emotional state by analyzing characteristics such as facial expressions and voice.

[0754] "Mobility devices" refer to equipment used to transport people, such as elevators and escalators.

[0755] "Specified operation" refers to actions or procedures that the system has pre-configured.

[0756] "Riding environment" refers to environmental elements that users perceive within the vehicle, such as lighting and music.

[0757] To realize this invention, the system is configured as follows: The server uses a small computer such as a Raspberry Pi or AWS cloud services to acquire schedule information and predict the next destination. Terminals installed in the corridor are equipped with a camera (such as a Logitech C920) and a microphone, and acquire video and audio data through face recognition using OpenCV and speech recognition using the Google Cloud Speech-to-Text API. Using this, an emotion analysis library is used to identify the emotional state of the user from their facial expressions and voice.

[0758] Based on the emotion analysis, the server determines the user's emotional state and, based on that, summons a mobility device (e.g., an elevator or a household robot) and automatically performs the specified operation. Furthermore, the server adjusts the ride environment, playing music and adjusting the lighting to ensure the user's comfort.

[0759] As a concrete example, when a user returns home tired, the lighting in the vehicle is immediately adjusted to a relaxing level, and soothing music is played. This reduces the stress that users experience in their daily lives and provides a more comfortable environment.

[0760] An example of a prompt for a generative AI model is, "Please give me some ideas for programming an application that will enable a home robot to recognize the user's emotions and provide appropriate support." Using this prompt, insights can be gained regarding the development of robot applications with emotion recognition capabilities.

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The server retrieves user schedule information from the database. It receives a user ID as input and retrieves the appointments and activity plans associated with that ID. As output, it obtains schedule information that serves as the basis for predicting the user's next destination. Based on this information, it calculates the predicted destination the user will take.

[0764] Step 2:

[0765] The terminal acquires video data using cameras installed in the corridor. It receives real-time video as input and performs face recognition using OpenCV. As output, it identifies the user's face and extracts feature vectors from the face image. These feature vectors are used later for sentiment analysis.

[0766] Step 3:

[0767] The device acquires audio data using a microphone. As input, it records the user's voice in real time and converts it to text using the Google Cloud Speech-to-Text API. As output, text data is generated, and its tone is used for sentiment analysis. This audio data also contributes to identifying emotional states.

[0768] Step 4:

[0769] The server uses an emotion analysis library to identify the user's emotional state from the facial features and audio text data obtained in steps 2 and 3. It receives facial features and voice tone as input and performs analysis using an emotion model. The identified emotional state is obtained as output. This allows for an understanding of the user's current mood and psychological state.

[0770] Step 5:

[0771] The server uses emotion analysis results and schedule information to summon and automate the operation of the mobile device. It selects the optimal mobile device using predicted destinations and emotional state information as input. The output includes summoning the mobile device and adjusting its waiting time. This operation reduces user stress and efficiently guides them to their destination.

[0772] Step 6:

[0773] When a user boards a mobile device, the server sends instructions to adjust the environment. It receives an identified emotional state as input and determines music and lighting settings accordingly. As output, the environment within the mobile device is automatically adjusted, providing a comfortable space for the user. This operation may further improve the user's mood.

[0774] 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.

[0775] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0776] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0777] 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.

[0778] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0779] 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.

[0780] 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.

[0781] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0782] 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."

[0783] 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.

[0784] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0785] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0794] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] A means of obtaining the user's schedule information and predicting their next destination,

[0798] A means of acquiring video data from an imaging device installed in a passageway and performing facial recognition,

[0799] A means for calling an elevator and automatically performing a specified operation based on a predicted destination,

[0800] A means of automatically setting the destination floor when a passenger boards,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, further comprising means for comparing user identification information obtained by facial recognition with schedule information to improve accuracy.

[0804] (Claim 3)

[0805] The system according to claim 1, further comprising means for selecting the optimal elevator based on the operating status and location information of the elevator.

[0806] "Example 1"

[0807] (Claim 1)

[0808] A means of acquiring user activity information and estimating the next destination,

[0809] A means of acquiring photographic data from an installed recording device and utilizing identification technology,

[0810] A means for controlling an elevator and automatically performing specific operations based on an estimated destination,

[0811] A means of automatically setting the destination level when a user boards,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, further comprising means for matching user information obtained by identification technology with activity information to improve accuracy.

[0815] (Claim 3)

[0816] The system according to claim 1, further comprising means for selecting the optimal elevator based on the operating status and location information of the elevators.

[0817] "Application Example 1"

[0818] (Claim 1)

[0819] A means of sending notifications to optimize the user's progress,

[0820] A means of determining location using the location information of the user's terminal,

[0821] A means of accessing a cloud-based data management system to retrieve schedule and operational information,

[0822] A means of identifying users using facial recognition technology,

[0823] A means of sending notifications to optimize elevator usage,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, further comprising means for analyzing user identification information using a program model and providing guidance for an efficient route.

[0827] (Claim 3)

[0828] The system according to claim 1, further comprising means for transmitting route information to the user's mobile device before the user reaches the building, thereby assisting travel from the starting point to the destination.

[0829] "Example 2 of combining an emotion engine"

[0830] (Claim 1)

[0831] A means of obtaining the user's scheduled time information and predicting the next destination,

[0832] A means of acquiring visual information from image acquisition devices installed along the route and performing personal identification,

[0833] A means for calling a vertical transport device and automatically performing a specific operation based on a predicted destination,

[0834] A means of automatically setting the destination group when a user boards,

[0835] A means for analyzing the emotional state of users and adjusting the operation of the vertical transport device,

[0836] A means of adjusting the internal environment of a vertical transport device to provide users with a comfortable environment,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, further comprising means for comparing user identification information obtained through personal recognition with time schedule information to improve the accuracy of the analysis.

[0840] (Claim 3)

[0841] The system according to claim 1, further comprising means for selecting the optimal vertical transport device based on the operating status and location information of the vertical transport device.

[0842] "Application example 2 when combining with an emotional engine"

[0843] (Claim 1)

[0844] A means of obtaining the user's schedule information and predicting their next destination,

[0845] A means of acquiring video data from an imaging device installed in a passageway and performing emotion analysis,

[0846] A means for calling a mobile device and automatically performing a specified operation based on a predicted destination,

[0847] A means of adjusting the boarding environment according to the emotional state of passengers during the ride,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, further comprising means for comparing user identification information obtained by facial recognition with schedule information and emotional information to improve accuracy.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising means for selecting the optimal mobile device based on the operating status and location information of the mobile device, and for adjusting the waiting time in consideration of the user's emotional state. [Explanation of Symbols]

[0853] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of sending notifications to optimize the user's progress, A means of determining location using the location information of the user's terminal, A means of accessing a cloud-based data management system to retrieve schedule and operational information, A means of identifying users using facial recognition technology, A means of sending notifications to optimize elevator usage, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing user identification information using a program model and providing guidance for an efficient route.

3. The system according to claim 1, further comprising means for transmitting route information to the user's mobile device before the user reaches the building, thereby assisting travel from the starting point to the destination.

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