System and method for providing guided service solution to address component failures in system for moving persons

Abstract

The invention relates to a system and method for providing a guided service solution to address component failures in a system for moving personnel. Disclosed is a system for moving a person, comprising: a member; and a processor configured to: receive a diagnostic message identifying a fault of the component from the mobile device; and executing a Generative Artificial Intelligence (Gen-AI) model that identifies the component, the fault and generates service solution data for executing a service solution for solving the fault for the component; and transmitting the service solution data to the mobile device.

Description

Technical Field

[0001] Embodiments described herein are directed to systems for moving people, including but not limited to elevators, escalators, and moving walkways, and more particularly to systems and methods for providing guided service solutions to resolve component failures in such systems. Background Art

[0002] A mechanic may use a software application to troubleshoot a fault. Messages from such an application may require the mechanic to understand the system used to move personnel to understand and resolve the fault. Such an application may also provide a brief description of the fault without providing information on how to resolve the fault. Summary of the invention

[0003] A system for mobile personnel is disclosed, comprising: a component; and a processor configured to: receive a diagnostic message identifying a fault of the component from a mobile device; and execute a generative artificial intelligence (Gen-AI) model, which identifies the component, the fault and generates service solution data for executing a service solution for solving the fault for the component; and transmit the service solution data to the mobile device.

[0004] Additionally or alternatively to one or more of the above aspects of the system, the component is an elevator car, an elevator machine, an escalator, or a moving walkway.

[0005] In addition to or in the alternative to one or more of the above aspects of the system, the Gen-AI architecture is configured to identify a component from a pairwise mapping of an image and a text encoder matrix, the mapping being generated by applying a component image description to a text encoder of the Gen-AI architecture and applying the component image to an image encoder of the Gen-AI architecture.

[0006] Additionally or alternatively to one or more of the above aspects of the system, the diagnostic message includes a textual description of a symptom of the fault; and the Gen-AI framework includes an encoder that extracts the component and the fault from the textual description.

[0007] Additionally or alternatively to one or more of the above aspects of the system, the service solution data includes textual instructions and corresponding graphical instructions for executing the service solution.

[0008] In addition to or as an alternative to one or more of the above aspects of the system, the textual instructions include a series of steps, and the graphical instructions illustrate the series of steps as one or more of an image and an animation for performing each step in the series of steps.

[0009] In addition to or as an alternative to one or more of the above aspects of the system, the graphical instructions illustrate the series of steps as a 3-D animation for performing each of the series of steps.

[0010] In addition to or in the alternative to one or more of the above aspects of the system, the Gen-AI architecture is configured to apply a text-to-text model to generate text instructions and to apply a text-to-image model to generate graphic instructions.

[0011] In addition to or in the alternative to one or more of the above aspects of the system, the processor is configured to train the Gen-AI architecture using training data from a database, the training data comprising: technical documentation and design specifications for the component; service history data for the component, which includes previous failures and previous service solutions; and previous feedback data related to previous service solutions, wherein after executing the Gen-AI architecture to identify a service solution, the service history data is updated to include the service solution and the feedback data for the service solution.

[0012] Additionally or alternatively to one or more of the above aspects of the system, the service history data in the database is grouped by geographic region.

[0013] Also disclosed is a method for resolving a failure of a component in a system for mobile personnel, comprising: receiving a diagnostic message identifying a failure of a component from a mobile device; and executing a generative artificial intelligence (Gen-AI) model that identifies the component, the failure, and generates service solution data for executing a service solution for resolving the failure of the component; and transmitting the service solution data to the mobile device.

[0014] Additionally or alternatively to one or more of the above aspects of the method, the component is an elevator car, an elevator machine, an escalator or a moving walkway.

[0015] In addition to or in the alternative to one or more of the above aspects of the method, the method includes identifying a component from a pairwise mapping of an image and a text encoder matrix, the mapping generated by applying a component image description to a text encoder of the Gen-AI architecture and applying the component image to an image encoder of the Gen-AI architecture.

[0016] In addition to or in the alternative to one or more of the above aspects of the method, the diagnostic message includes a text description of a symptom of the fault; and the method includes the Gen-AI architecture extracting the component and the fault from the text description within the diagnostic message using an encoder.

[0017] Additionally or alternatively to one or more of the above aspects of the method, the method comprises including in the service solution data text instructions and corresponding graphical instructions for executing the service solution.

[0018] Additionally or alternatively to one or more of the above aspects of the method, the textual instructions include a series of steps; and the method includes providing, using the graphical instructions, one or more of an image and an animation for performing each step in the series of steps.

[0019] Additionally or alternatively to one or more of the above aspects of the method, the method includes providing, using the graphical instructions, a 3-D animation for performing each of the series of steps.

[0020] In addition to or in the alternative to one or more of the above aspects of the method, the method includes applying, by the Gen-AI architecture, a text-to-text model to generate text instructions and applying a text-to-image model to generate graphic instructions.

[0021] In addition to or as an alternative to the above aspects of the method, the method includes training the Gen-AI architecture using training data from a database, the training data including: technical documentation and design specifications for the component; service history data for the component, which includes previous failures and previous service solutions; and previous feedback data related to the previous service solutions, wherein after executing the Gen-AI architecture to identify the service solution, the method includes updating the service history data to include the service solution and the feedback data for the service solution.

[0022] Additionally or alternatively to one or more of the above aspects of the method, the method includes grouping the service history data in the database by geographic region. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present disclosure is illustrated by way of examples and is not limited by the accompanying drawings, in which like references indicate similar elements.

[0024] Figure 1 is a schematic diagram of an elevator system in which various embodiments of the present disclosure may be employed;

[0025] Figure 2 A technology ecosystem for implementing a Gen-AI architecture for providing guided service solutions to resolve component failures is shown;

[0026] Figure 3 is a flow chart illustrating a pair-wise mapping of component descriptions and images for utilization by a Gen-AI architecture to generate a service solution from a description of a component and fault data;

[0027] Figure 4 is another flow chart illustrating additional details utilized by the Gen-AI architecture to generate a serving solution, including utilizing a diffusion model and an image decoder to generate an image that supports the serving solution;

[0028] Figure 5 is another flow chart showing additional details of utilizing the Gen-AI architecture to generate a service solution;

[0029] Figure 6 is a flow chart illustrating a method for providing a guided service solution to resolve component failures using a Gen-AI architecture; and

[0030] Figure 7 is a flow chart illustrating a method of training a Gen-AI architecture. DETAILED DESCRIPTION

[0031] Figure 1 1 is a perspective view of an elevator system 101, which includes an elevator car 103, a counterweight 105, a tension member 107, a guide rail (or rail system) 109, a machine (or machine system) 111, a position reference system 113, and an electronic elevator controller (controller) 115. The elevator car 103 and the counterweight 105 are connected to each other by the tension member 107. The tension member 107 may include or be configured as, for example, a rope, a steel cable, and / or a coated steel belt. The counterweight 105 is configured to balance the load of the elevator car 103 and to facilitate the movement of the elevator car 103 within the elevator shaft (or hoistway) 117 and along the guide rail 109 relative to the counterweight 105 simultaneously and in opposite directions.

[0032] The tension member 107 engages a machine 111, which is part of the top structure of the elevator system 101. The machine 111 is configured to control the movement between the elevator car 103 and the counterweight 105. The position reference system 113 can be mounted on a fixed portion at the top of the elevator shaft 117, such as on a support or guide rail, and can be configured to provide a position signal related to the position of the elevator car 103 within the elevator shaft 117. In other embodiments, the position reference system 113 can be mounted directly to the moving member of the machine 111, or can be located in other locations and / or configurations as known in the art. As known in the art, the position reference system 113 can be any device or mechanism for monitoring the position of the elevator car and / or counterweight. As will be appreciated by those skilled in the art, the position reference system 113 can be, for example but not limited to, an encoder, a sensor, or other system, and can include speed sensing, absolute position sensing, etc.

[0033] As shown, the controller 115 is located in a controller room 121 of the elevator shaft 117 and is configured to control the operation of the elevator system 101 and, in particular, the elevator car 103. It will be appreciated that the controller 115 need not be in the controller room 121, but may be in the hoistway or other locations in the elevator system. For example, the controller 115 may provide drive signals to the machine 111 to control the acceleration, deceleration, leveling, stopping, etc. of the elevator car 103. The controller 115 may also be configured to receive position signals from the position reference system 113 or any other desired position reference device. When moving up or down in the elevator shaft 117 along the guide rails 109, the elevator car 103 may stop at one or more landings 125 as controlled by the controller 115. Although shown in the controller room 121, those skilled in the art will appreciate that the controller 115 may be located and / or constructed at other locations or locations within the elevator system 101. In one embodiment, the controller may be located remotely or in the cloud.

[0034] The machine 111 may include a motor or similar drive mechanism. According to an embodiment of the present disclosure, the machine 111 is configured to include an electrically driven motor. The power supply for the motor may be any power source including a power grid that is combined with other components to supply the motor. The machine 111 may include a traction sheave that imparts force to the tension member 107 to move the elevator car 103 within the elevator shaft 117.

[0035] In other embodiments, the system includes a conveyor system that moves passengers between floors and / or along a single floor. Such conveyor systems may include escalators, people movers including moving walkways, etc. Therefore, the embodiments described herein are not limited to elevator systems such as Figure 1 The elevator system shown in .

[0036] Although a rope system including tension members 107 is shown and described, elevator systems that employ other methods and mechanisms for moving an elevator car within an elevator shaft may employ embodiments of the present disclosure. For example, embodiments may be employed in ropeless elevator systems that use linear motors to impart motion to an elevator car. Embodiments may also be employed in ropeless elevator systems that use hydraulic lifts to impart motion to an elevator car. Embodiments may also be employed in ropeless elevator systems that use self-propelled elevator cars (e.g., elevator cars equipped with friction wheels, pinch wheels, or traction wheels). Figure 1 Non-limiting examples are given for exemplary and explanatory purposes only.As indicated, escalators and moving walkways are within the scope of the present disclosure.

[0037] Go to Figure 2 In one embodiment, the mechanic 190 may identify Figure 1The mechanic 190 may have a cell phone 200 or other smart device having a screen 210 and a keyboard 220, such as a virtual keyboard, a touch screen, or an appliance for processing voice input.

[0038] The mechanic 190 can send a diagnostic message 196 identifying the component 195 and the symptom of the fault in plain text over the network 225 via the mobile device 200 for processing on a processor 230 located on a local or remote server 240 or on the cloud. The diagnostic message 196 can also be automatically generated by the mobile application (or other application running on the edge device) by processing sensor data received from the elevator car. For example, the mobile application can receive the sensor data and analyze the problem and generate the diagnostic message in an automated manner.

[0039] It will be appreciated that the mechanic 190 may observe the fault or realize that the fault exists based on sensor data, such as from sensors on the elevator system 101 (e.g., speed and vibration sensors). The processor 230 may include a generative artificial intelligence (Gen-AI) framework 250 that is executable to extract the identity and fault of the component 195 from the diagnostic message 196. Using the Gen-AI framework 250, the processor 230 may generate service solution data (or generally, service solution) 260. The service solution data 260 may be transmitted back to the mobile device 200 for guiding the mechanic 190 to resolve the fault of the component 195. The solution 260 may include text and images, such as 3-D animations. This enables the mechanic 190, who has relatively little knowledge of the elevator system 101, to resolve the fault, saving time and resources.

[0040] The Gen-AI framework 250 may be trained using training data 252 from a database 255, which includes technical documentation and design specifications for the component 195. The training data 252 may also include service history data for the component 195. The service history data may include previous failures and previous service solutions for the component 195. The training data 252 may also include previous feedback data related to previous service solutions for the component 195 provided by the mechanic 190 or other mechanics. The service history in the database 255 may be grouped by geographic region, for example, because different regions may have local configurations of the component 195 due to utilization habits or other local influences. After executing the Gen-AI framework 250 to identify the service solution 260, the service history data is updated to include the service solution 260 and the current feedback data 289 from the mechanic 190 for fine-tuning the Gen-AI framework 250.

[0041] More specifically, if Figure 3 As shown in , the Gen-AI architecture 250 can identify the component 195 from the paired mapping 270 of the image and text encoder matrices. The mapping 270 can be generated by applying the component image description 280 to the text encoder 290A (encoder generally referred to as 290) of the Gen-AI architecture 250 and applying the component image 285 to the image encoder 290B of the Gen-AI architecture 250.

[0042] like Figure 4 As shown in , applying the pairwise mapping 270, the disclosed Gen-AI architecture 250 can utilize the encoder 290 to extract text input 196 from the mobile device 200 and identify components 195 and faults. The output from the encoder 290 can be fed to a diffusion model 300 customized for use with elevators, escalators, and moving walkways. This model may include DALL-E developed by OpenAI to generate service solutions 260. by Adobe's Firefly (https: / / www.adobe.com / sensei / generative-ai / firefly.html); by Stability.AI's Dreamstudio stable diffusion model (https: / / github.com / Stability-AI / stablediffusion); by Midjourney's Midjourney Image Generator model (https: / / www.midjourney.com / home / ?callbackUrl=%2Fapp%2F); and by Microsoft Bing's Image Creator (https: / / www.bing.com / create?toWww=1&redig=E0276A5023D243858F49FAA1A5A4A8F5). The service solution 260 is then processed by the image decoder 310 to generate a mobile device 200 ( Figure 2 ) is displayed on the generated image 320.

[0043] More specifically, if Figure 5 As shown in , the Gen-AI architecture 250 receives the diagnostic message 196 and extracts the embedded text using the text encoder 290A. Figure 3As shown in , encoder 290 is used to identify component 195. Then, service solution 260 is generated, for example, using diffusion model 300. For example, grouped visual data 260A is obtained for component 195. A series of steps 260B for a solution to a fault (or problem) is generated. This can be achieved by applying a text-to-text model based on a large language model structure, such as using ChatGPT as a non-limiting example. For example, using a text-to-image model, a visual output 260C is generated for each step. Solution data 260 is continued to be generated for each step in the series of steps. Then, the Gen-AI architecture 250 generates a package of all images as a visual output, for example, as using box 260 ( Figure 5 ) as shown.

[0044] Go to Figure 6 , a flow chart shows a method for providing a guided service solution to resolve an elevator component failure. As shown in block 610, the method includes receiving a diagnostic message identifying a failure of a component 195 of an elevator system 101 from a mobile device 200. As shown in block 620, the method includes executing a generative artificial intelligence (Gen-AI) framework 250 that identifies the component 195, the failure, and generates service solution data 260 for resolving the failure for the component 295.

[0045] As shown in block 620A, identifying a component (block 620) includes utilizing a pairwise mapping 270 of an image and text encoder matrix generated by applying a component image description 280 to a text encoder 290 of the Gen-AI architecture 250 and applying a component image 285 to an image encoder 290B of the Gen-AI architecture 250 ( Figure 3 As shown in block 620B, identifying components and faults (block 620) includes extracting components 195 and faults ( Figure 4 ).

[0046] As shown in block 620C, generating the service solution data (block 620) includes providing text instructions and corresponding graphical instructions ( ) for executing the service solution in the service solution data 260. Figure 5). As shown in block 620D, generating service solution data (block 620) further includes providing one or more of an image and an animation for performing each step in the series of steps using graphical instructions. As shown in block 620E, generating service solution data (block 620) further includes providing a 3-D animation for performing each of the series of steps using graphical instructions. Alternatively, images and animations may be provided for steps of particular importance, such as by weighting. As shown in block 620F, generating service solution data (block 620) further includes applying, by the Gen-AI architecture 250, a text-to-text model to generate text instructions and applying a text-to-video / animation model to generate graphical instructions. It will be appreciated that block 260 ( Figure 5 ) covers the packaging of multiple images for animation purposes.

[0047] As shown in block 630 , the method includes transmitting the service solution data 260 to the mobile device 200 for review by the mechanic 190 .

[0048] like Figure 7 As shown in , a flowchart illustrates a method for training a Gen-AI architecture 250 to provide a guided service solution to resolve an elevator component failure. As shown in box 710, the method includes training the Gen-AI architecture 250 using training data 252 from a database 255, which includes technical documents and design specifications for the component. The database 255 also includes service history data for the component, including previous failures, previous service solutions, and previous feedback data related to the previous service solutions. As shown in box 710A, training the Gen-AI architecture 250 (box 710) further includes grouping the service history in the database 255 by geographic region. As shown in box 720, after executing the Gen-AI architecture 250 to identify a service solution, the method includes updating the service history data to include the service solution and the feedback data 289 obtained for the service solution, and then using it to further improve the model and future solutions.

[0049] As identified in this article, the architecture provides working parameters—such as the number, size, and type of layers in a neural network. A model may represent a part of the architecture; an instance trained on a selected set of data. For example, in a neural network, the training weights of each node may constitute a model according to the architecture. Generative AI is an artificial intelligence that can learn from existing achievements to generate new, real achievements (on a large scale) that reflect the characteristics of the training data. It can generate a variety of novel content, such as images, videos, music, speech, text, software code, and product design. The generative AI model learns the pattern and structure of its input training data, and then generates new data with similar characteristics. Diffusion models are a class of latent variable models trained using variable reasoning. Diffusion models learn the potential structure of a data set by modeling the way data points diffuse through a latent space. Diffusion models can be applied to a variety of tasks, including image denoising, restoration, super-resolution, and image generation. An encoder is a neural network that converts input data into a lower dimensional representation. A decoder is a neural network that reconstructs input data from a lower dimensional representation. Encoders and decoders are used together to reduce dimensions and extract features from data.

[0050] The sensor data identified herein may be acquired and processed individually, or acquired and stitched together, or a combination of both, and may be processed in raw or compiled form. The sensor data may be processed at the sensor (e.g., via edge computing), by a controller identified or referred to herein, on a cloud service, or by a combination of one or more of these computing systems. The sensor may transmit data via a wired or wireless transmission line using one or more protocols as indicated below.

[0051] Wireless connection can apply the protocol including local area network (LAN or WLAN for wireless LAN) protocol. LAN protocol includes WiFi technology based on Section 802.11 standard from the Institute of Electrical and Electronics Engineers (IEEE). Other applicable protocols include low power WAN (LPWAN), which is a wireless wide area network (WAN) designed to allow remote communication at low bit rates so that terminal devices can operate using battery power for extended periods of time (several years). Long-range WAN (LoRaWAN) is a LPWAN maintained by the LoRa Alliance and is a media access control (MAC) layer protocol for transmitting management and application messages between network servers and application servers, respectively. LAN and WAN protocols can be generally considered to be TCP / IP protocols (Transmission Control Protocol / Internet Protocol) for managing the connection of computer systems to the Internet. Wireless connection can also apply the protocol including private area network (PAN) protocol. PAN protocol, for example, includes Bluetooth Low Energy (BTLE), which is a wireless technology standard designed and sold by the Bluetooth Special Interest Group (SIG) for exchanging data over short distances using short wavelength radio waves. PAN protocols also include Zigbee, a technology based on the Section 802.15.4 protocol from the IEEE, representing a set of advanced communication protocols for creating personal area networks with small, low-power digital radios for low-power, low-bandwidth needs. Such protocols also include Z-Wave, a wireless communication protocol supported by the Z-Wave Alliance that uses mesh networking to apply low-energy radio waves to communicate between devices (such as appliances), allowing them to be controlled wirelessly.

[0052] The wireless connection may also include radio frequency identification (RFID) technology, which is used to communicate with an integrated chip (IC) on, for example, an RFID smart card. In addition, RF devices below 1 GHz operate in the ISM (industrial, scientific and medical) spectrum band below 1 GHz (usually within the frequency range of 769-935 MHz, 315 MHz and 468 MHz). This spectrum band below 1 GHz is particularly useful for RF IOT (Internet of Things) applications. The Internet of Things (IoT) describes a network of physical objects ("things"), embedded with sensors, software and other technologies for the purpose of connecting and exchanging data with other devices and systems via the Internet. Other LPWAN-IOT technologies include narrowband Internet of Things (NB-IOT) and type M1 Internet of Things (Cat M1-IOT). Wireless communications for the disclosed system may include cellular, such as 2G / 3G / 4G, etc. Other wireless platforms based on RFID technology include near field communication (NFC), which is a group communication protocol for low-speed communication, for example, to exchange data between electronic devices over a short distance. NFC standards are defined by ISO / IEC (defined below), NFC Forum and GSMA (Global System for Mobile Communications) organizations. The above is not intended to limit the scope of applicable wireless technologies.

[0053] Wired connections may include connections (cables / interfaces) under RS ​​(Recommended Standard)-422, also known as TIA / EIA-422, which is a technical standard supported by the Telecommunications Industry Association (TIA) and originated from the Electronic Industries Alliance (EIA) that specifies the electrical characteristics of digital signal circuits. Wired connections may also include (cables / interfaces) under the RS-232 standard for serial communication transmission of data, which formally defines the signal connection between a DTE (data terminal equipment) such as a computer terminal and a DCE (data circuit-terminating equipment or data communications equipment) such as a modem. Wired connections may also include connections (cables / interfaces) under the Modbus serial communication protocol managed by the Modbus organization. Modbus is a master / slave protocol designed to be used with its programmable logic controller (PLC) and is a common means of connecting industrial electronic devices. Wireless connections may also include connectors (cables / interfaces) under the PROFibus (process fieldbus) standard managed by PROFIBUS & PROFINET International (PI). PROFibus is a fieldbus communication standard in automation technology, publicly released as part of IEC (International Electrotechnical Commission) 61158. Wired communication can also be through the Controller Area Network (CAN) bus. CAN is a carrier bus standard that allows microcontrollers and devices to communicate with each other in applications without a host. CAN is a message-based protocol published by the International Standards Organization (ISO). The above is not intended to limit the scope of applicable wired technologies.

[0054] When data is transmitted over a network between terminal processors as identified herein, the data may be transmitted in raw form, or may be processed in whole or in part at any of the terminal processors or intermediate processors, for example, at a cloud service (e.g., where at least a portion of the transmission path is wireless) or at another processor. The data may be parsed, partially or fully processed or compiled on any of the processors, and may then be spliced ​​together or maintained as separate packets of information. Each processor or controller identified herein may be, but is not limited to, a single processor or multi-processor system in any of a large number of possible architectures, including field programmable gate arrays (FPGAs), central processing units (CPUs), application specific integrated circuits (ASICs), digital signal processors (DSPs), or graphics processing units (GPUs) hardware in homogeneous or heterogeneous arrangements. The memory identified herein may be, but is not limited to, random access memory (RAM), read-only memory (ROM), or other electronic, optical, magnetic, or any other computer-readable medium.

[0055] In addition to the processor and non-volatile memory, the controller may further include one or more input and / or output (I / O) device interfaces, which are connected to communicate between other devices via an onboard (local) interface communication. The onboard interface may include, for example, but not limited to, an onboard system bus, including a control bus (for communication between devices), an address bus (for physical addressing), and a data bus (for transmitting data). That is, the system bus can realize electronic communication between the processor, the memory, and the I / O connection. The I / O connection may also include a wired connection and / or a wireless connection identified herein. The onboard interface may have additional elements such as a controller, a buffer (cache), a driver, a repeater, and a receiver (for simplicity, these elements are omitted) to realize electronic communication. The memory can execute a program, access data or a lookup table, or a combination thereof, all of which can be pre-stored or received during the execution of its process by other computing devices in order to facilitate its processing, for example, via a cloud service or other network connections with other processors identified herein.

[0056] Embodiments may be in the form of processor-implemented processes and apparatus (such as a processor) for practicing those processes. Embodiments may also be in the form of computer code-based modules, for example, the instructions contained in the computer program code (e.g., a computer program product) are embodied in a tangible medium (e.g., a non-transitory computer-readable medium), such as a floppy disk, a CDROM, a hard drive, a register on a processor as firmware, or any other non-transitory computer-readable medium, wherein when the computer program code is loaded into a computer and executed by the computer, the computer becomes an apparatus for practicing the embodiments. Embodiments may also be in the form of computer program code, for example, stored in a storage medium, loaded into a computer and / or executed by a computer, or transmitted over some transmission medium, such as on a wire or cable, through an optical fiber, or via electromagnetic radiation, wherein when the computer program code is loaded into a computer and executed by the computer, the computer becomes an apparatus for practicing the exemplary embodiments. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create a specific logic circuit.

[0057] The terms used herein are only used for the purpose of describing specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising" when used in this specification indicate the presence of the indicated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0058] Those skilled in the art will recognize that various exemplary embodiments are shown and described herein, each of which has certain features in a particular embodiment, but the present disclosure is not limited thereto. Rather, the present disclosure may be modified to incorporate any number of variations, modifications, substitutions, combinations, sub-combinations, or equivalent arrangements not previously described but commensurate with the scope of the present disclosure. Additionally, although various embodiments of the present disclosure have been described, it will be understood that aspects of the present disclosure may include only some of the described embodiments. Therefore, the present disclosure is not to be viewed as limited by the foregoing description, but only by the scope of the appended claims.

Claims

1. A system for moving personnel, comprising: member; as well as A processor configured to: receiving a diagnostic message from a mobile device identifying a fault with the component; as well as executing a generative artificial intelligence (Gen-AI) model that identifies the component, the fault, and generates service solution data for executing a service solution for resolving the fault for the component; as well as The service solution data is transmitted to the mobile device.

2. The system according to claim 1, wherein: The component is an elevator car, an elevator machine, an escalator or a moving walkway.

3. The system of claim 1, wherein: The Gen-AI architecture is configured to identify the component from a pair-wise mapping of an image and a text encoder matrix, the mapping being generated by applying a component image description to a text encoder of the Gen-AI architecture and applying a component image to an image encoder of the Gen-AI architecture.

4. The system of claim 1, wherein: The diagnostic message includes a textual description of the symptoms of the fault; and The Gen-AI architecture includes an encoder that extracts the component and the fault from the text description.

5. The system of claim 4, wherein: The service solution data includes text instructions and corresponding graphic instructions for executing the service solution.

6. The system of claim 5, wherein: The text instructions include a series of steps, and the graphical instructions illustrate the series of steps as one or more of an image and an animation for performing each step in the series of steps.

7. The system according to claim 6, wherein: The graphics instructions illustrate the series of steps as a 3-D animation for performing each of the series of steps.

8. The system of claim 5, wherein: The Gen-AI architecture is configured to apply a text-to-text model to generate the text instructions and to apply a text-to-image model to generate the graphic instructions.

9. The system of claim 5, wherein: The processor is configured to train the Gen-AI framework using training data from a database, the training data comprising: Technical documentation and design specifications for the components; service history data for the component, including previous failures and previous service resolutions; and previous feedback data related to said previous service resolution, After executing the Gen-AI framework to identify the service solution, the service history data is updated to include the service solution and feedback data for the service solution.

10. The system according to claim 9, wherein: The service history data in the database are grouped by geographical area.

11. A method of resolving a failure of a component in a system for moving personnel, comprising: receiving a diagnostic message from a mobile device identifying a fault with the component; as well as executing a generative artificial intelligence (Gen-AI) model that identifies the component, the fault, and generates service solution data for executing a service solution for resolving the fault for the component; as well as The service solution data is transmitted to the mobile device.

12. The method according to claim 11, wherein: The component is an elevator car, an elevator machine, an escalator or a moving walkway.

13. The method according to claim 11, comprising: The component is identified from a pairwise mapping of an image and a text encoder matrix, the mapping being generated by applying a component image description to a text encoder of a Gen-AI architecture and applying a component image to an image encoder of the Gen-AI architecture.

14. The method according to claim 11, wherein: The diagnostic message includes a textual description of the symptoms of the fault; and The method includes extracting the component and the fault from a text description within the diagnostic message using an encoder by the Gen-AI architecture.

15. The method according to claim 14, comprising: The service solution data includes text instructions and corresponding graphic instructions for executing the service solution.

16. The method of claim 15, wherein: The text instructions include a series of steps; and The method includes providing, using the graphics instructions, one or more of an image and an animation for performing each step in the series of steps.

17. The method according to claim 16, wherein: The method includes providing, using the graphics instructions, a 3-D animation for performing each of the series of steps.

18. The method according to claim 15, comprising: The Gen-AI architecture applies a text-to-text model to generate text instructions, and applies a text-to-image model to generate graphic instructions.

19. The method according to claim 15, comprising: The Gen-AI architecture is trained using training data from a database, the training data comprising: Technical documentation and design specifications for the components; service history data for the component, including previous failures and previous service resolutions; and previous feedback data related to said previous service resolution, Wherein, after executing the Gen-AI framework to identify the service solution, the method includes updating the service history data to include the service solution and feedback data for the service solution.

20. The method of claim 19, comprising grouping the service history data in the database by geographic region.

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