Assisted transport system problem resolution

Abstract

The subject matter of the invention is "Assisted Transport System Problem Resolution". A method of providing assisted problem resolution for a transportation system, the method comprising: receiving, at a prediction system, a problem description of a problem at the transportation system and current configuration parameters of the transportation system; determining, at the prediction system, at least one set of updated configuration parameters directed to solving the problem; and sending, from the prediction system to the user device, a response including the at least one set of updated configuration parameters.

Description

Technical Field

[0001] Embodiments disclosed herein relate generally to transportation system problem solving, and more particularly, to paratransit system problem solving. Background Art

[0002] When a transportation system (e.g., an elevator system) is not operating correctly, the transportation system may require problem solving. A technician will need to analyze transportation system related problems reported in the field. This is often combined with experience and subject matter experts to provide guidance for updating the configuration parameters of the transportation system. This "deep" analysis may require several days and multiple site visits to test different configuration parameters in the field. In addition, when the technician is on site at the transportation system, sometimes they may not know the (one or more) confirmed elevator configuration parameters. Summary of the invention

[0003] According to an embodiment, a method for providing assisted problem solving for a transportation system includes: receiving, at a prediction system, a problem description of a problem at a transportation system and current configuration parameters of the transportation system; at the prediction system, determining at least one set of updated configuration parameters directed at solving the problem; and sending a response including the at least one set of updated configuration parameters from the prediction system to a user device.

[0004] In addition to or as an alternative to one or more of the features described herein, further embodiments may include using one or more of the following to train the prediction system: engineering specifications, service records, and customer complaints.

[0005] In addition or as an alternative to one or more of the features described herein, further embodiments may include wherein the response comprises an impact analysis of the at least one set of updated configuration parameters on the transportation system.

[0006] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the response includes one or more causes of the problem.

[0007] In addition to or as an alternative to one or more of the features described herein, further embodiments may include operating the transport system in a trial run using the at least one set of updated configuration parameters.

[0008] In addition to or as an alternative to one or more of the features described herein, further embodiments may include saving the at least one set of updated configuration parameters when the commissioning indicates a successful resolution of the problem.

[0009] In addition to or as an alternative to one or more of the features described herein, further embodiments may include providing feedback to the prediction system after a trial run.

[0010] In addition to or as an alternative to one or more of the features described herein, further embodiments may include the prediction system performing adaptive learning based on feedback.

[0011] In addition to one or more of the features described herein, or as an alternative, further embodiments may include the at least one set of updated configuration parameters including multiple sets of updated configuration parameters, the method further comprising: selecting one of the multiple sets of updated configuration parameters as the selected set of updated configuration parameters; operating the transportation system in a trial run using the selected set of updated configuration parameters; and when the trial run indicates a successful resolution of the problem, saving the selected set of updated configuration parameters.

[0012] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the transportation system is one of an elevator, an escalator, and a moving walkway.

[0013] According to another embodiment, a system for providing assisted problem solving for a transportation system includes a prediction system that communicates with a user device via a network, the prediction system being configured to perform operations including: receiving, at the prediction system, a problem description of a problem at the transportation system and current configuration parameters of the transportation system; determining, at the prediction system, at least one set of updated configuration parameters directed at solving the problem; and sending, from the prediction system to the user device, a response including the at least one set of updated configuration parameters.

[0014] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the operations further include training the prediction system using one or more of: engineering specifications, service records, and customer complaints.

[0015] In addition or as an alternative to one or more of the features described herein, further embodiments may include wherein the response comprises an impact analysis of the at least one set of updated configuration parameters on the transportation system.

[0016] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the response includes one or more causes of the problem.

[0017] In addition or as an alternative to one or more of the features described herein, further embodiments may include wherein the transport system is operated in a trial run using the at least one set of updated configuration parameters.

[0018] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the operations further include receiving feedback at the prediction system after the commissioning.

[0019] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the operations further include the prediction system performing adaptive learning based on the feedback.

[0020] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the transportation system is one of an elevator, an escalator, and a moving walkway.

[0021] According to another embodiment, a computer program product embodied on a non-transitory computer-readable storage medium includes instructions for causing a processor to implement a process for providing assisted problem solving for a transportation system, the process including: receiving, at a prediction system, a problem description of a problem at the transportation system and current configuration parameters of the transportation system; determining, at the prediction system, at least one set of updated configuration parameters directed at solving the problem; and sending a response including the at least one set of updated configuration parameters from the prediction system to a user device.

[0022] Unless otherwise explicitly indicated, the aforementioned features and elements may be combined in various combinations without exclusivity. These features and elements and their operation will become more apparent from the following description and accompanying drawings. However, it should be understood that the following description and accompanying drawings are intended to be illustrative and explanatory in nature, rather than restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0025] Figure 2 A system for providing assisted transportation system problem solving in an example embodiment is depicted.

[0026] Figure 3 A method of providing a paratransit system problem solution in an example embodiment is described.

[0027] Figure 4 A computing system in an example embodiment is illustrated. DETAILED DESCRIPTION

[0028] Figure 1 1 is a perspective view of a transport system 101 including an elevator car 103, a counterweight 105, a tension member 107, a guide rail 109, a machine 111, a position reference system 113, and a controller 115. The elevator car 103 and the counterweight 105 are interconnected 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 movement of the elevator car 103 in an elevator shaft or hoistway 117 and along the guide rail 109 relative to the counterweight 105 simultaneously and in opposite directions.

[0029] The tension member 107 engages a machine 111 that is part of the overhead structure of the transport system 101. The machine 111 is configured to control movement between the elevator car 103 and the counterweight 105. The position reference system 113 can be mounted on a fixed portion of the top of the elevator shaft 117, such as on a support or 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 components of the machine 111, or can be located in other locations and / or configurations 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 known in the art. For example, but not limited to, the position reference system 113 can be an encoder, a sensor, or other system, and can include speed sensing, absolute position sensing, etc., as will be appreciated by those skilled in the art.

[0030] As shown, the controller 115 can be located in a controller room 121 of the elevator shaft 117 and is configured to control the operation of the transportation system 101, and in particular the operation of the elevator car 103. It is to be appreciated that the controller 115 need not be in the controller room 121, but can be in the hoistway or other locations in the transportation system 101. For example, the controller 115 can provide drive signals to the machine 111 to control the acceleration, deceleration, leveling, stopping, etc. of the elevator car 103. The controller 115 can 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 can 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 can be located and / or configured at other locations or locations within the transportation system 101. In one embodiment, the controller 115 may be remotely located or located in a distributed computing network (eg, a cloud computing architecture). The controller 115 may be implemented using a processor-based machine, such as a personal computer, a server, a distributed computing network, or the like.

[0031] 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 electric drive motor. The power source for the motor may be any power source, including an electrical grid, which, in combination with other components, is supplied to the motor. The machine 111 may include a traction sheave that applies force to the tension member 107 to move the elevator car 103 within the elevator shaft 117.

[0032] The transportation system 101 also includes one or more elevator doors 104. The elevator doors 104 can be attached to the elevator car 103, or the elevator doors 104 can be located on a landing 125 of the elevator system 101, or both. The embodiments disclosed herein can be applied to elevator doors 104 attached to the elevator car 103 or elevator doors 104 located on a landing 125 of the elevator system 101, or both. The elevator doors 104 open to allow passengers to enter and exit the elevator car 103.

[0033] Although shown and described with a roping system including tension members 107, 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 used in ropeless elevator systems that use linear motors to impart motion to an elevator car. Embodiments may also be used in ropeless transportation systems that use hydraulic lifts to impart motion to an elevator car. Embodiments may also be used in ropeless transportation systems that use self-propelled elevator cars (e.g., elevator cars equipped with friction wheels, pinch wheels, or traction wheels). Figure 1These are non-limiting examples presented for purposes of illustration and explanation only.

[0034] In other embodiments, the transportation system includes a transportation system that moves passengers between floors and / or along a single floor. Such transportation systems may include escalators, people movers, moving walkways, etc. Therefore, the embodiments described herein are not limited to such as Figure 1 An elevator transportation system such as the elevator transportation system shown in .

[0035] Figure 2 A system 200 for providing assisted transportation system problem solving in an example embodiment is depicted. The system includes a user device 202 to be operated by a user, such as a maintenance person. The user device 202 may be a mobile smart device, such as a phone, tablet, laptop, etc., having a processor, memory, display, etc. The user device 202 may also be implemented using a fixed device, such as a personal computer, server, remote terminal, etc.

[0036] The user device 202 is used during problem solving of the transportation system 101. During problem solving, the user device 202 communicates with the controller 115. The user device 202 may communicate with the controller 115 via a wired and / or wireless connection (e.g., WiFi, Bluetooth, NFC, LAN, CAN bus, cellular, satellite, Internet, etc.). The user device 202 may access configuration parameters, maintenance records, faults, errors, alarms, etc. stored on the controller 115.

[0037] The user device 202 also communicates with the prediction system 214 via a network 210. The network 210 may be implemented via one or more networks, such as, but not limited to, one or more of: WiMax, a local area network (LAN), a wireless local area network (WLAN), a personal area network (PAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), a wireless wide area network (WWAN), or any broadband network, and may also be enabled using technologies such as, for example, Global System for Mobile Communications (GSM), Personal Communications Service (PCS), Bluetooth, WiFi, fixed wireless data, 2G, 2.5G, 3G (e.g., 3G networks based on WCDMA / UMTS), 4G, IMT-Advanced, pre-4G, 5G, LTE Advanced, Mobile WiMax, WiMax 2. WirelessMAN-Advanced Networks, Enhanced Data rates for GSM Evolution (EDGE), General Packet Radio Service (GPRS), Enhanced GPRS, iBurst, UMTS, HSPDA, HSUPA, HSPA, HSPA+, UMTS-TDD, 1xRTT, EV-DO, messaging protocols such as TCP / IP, SMS, MMS, Extensible Messaging and Presence Protocol (XMPP), Real-time Messaging Protocol (RTMP), Instant Messaging and Presence Protocol (IMPP), Instant Messaging, USSD, IRC, or any other wireless data network, broadband network, or messaging protocol.

[0038] In some embodiments, user device 202 executes an application supported by prediction system 214. In these embodiments, the user can connect to prediction system 214 using the application installed on the user device. In other embodiments, prediction system 214 is accessed using a general purpose interface such as a web browser.

[0039] The prediction system 214 may be embodied as any type of processor-based computing or computer device capable of performing the functions described herein, including but not limited to computers, servers, workstations, desktop computers, laptop computers, notebook computers, tablet computers, mobile computing devices, wearable computing devices, network appliances, web appliances, distributed computing systems (e.g., cloud computing), processor-based systems, and / or consumer electronic devices.

[0040] The prediction system 214 may be implemented based on a model of the transportation system 101 trained using engineering specifications, service records, customer complaints, (one or more) current and past controller parameter sets, operating conditions (data collected from sensors), usage patterns, and maintenance history. The prediction system 214 may be implemented using an artificial intelligence (AI) system such as a large language model (LLM). As an LLM, the prediction system 214 may receive queries in natural language and provide outputs in natural language. The prediction system 214 may use deep learning techniques for initial training and adaptive learning as the transportation vehicle operates over time.

[0041] The database 218 communicates with the prediction system 214 and the user device 202 via the network 210. The database 218 may be implemented using known storage devices such as random access memory (RAM), read-only memory (ROM), flash memory, hard disk drive (HDD), solid state drive (SSD), etc. The database 218 may be implemented using various technologies including a memory device in the prediction system 214, a memory device remote from the prediction system 214, cloud storage, etc.

[0042] Figure 3 A method for providing auxiliary transportation system problem solving in an example embodiment is depicted. The process begins at 300, where the prediction system 214 is trained. The prediction system 214 can be trained using a knowledge base of the transportation system 101 including one or more of the following: engineering specifications, service records, customer complaints, etc. The prediction system 214 can also collect various data points from the controller 115, including data from elevator sensors, including but not limited to speed, temperature, door movement, load capacity, and energy consumption. In addition, data from usage patterns, such as peak usage times and maintenance history, can be considered. Training can be performed using a machine learning algorithm to recognize patterns.

[0043] The machine learning training process may include data preprocessing to convert the raw data form into a digital form suitable for the machine learning process. This may include one or more of the following: tokenization of natural language text derived from engineering specifications, service records, customer complaints; current and past controller parameter sets as digital vectors; operating conditions (data collected from sensors) to digital vectors; and usage pattern and maintenance history data as digital vectors.

[0044] The machine learning training process can include data training and validation data preparation, which splits the entire available dataset into two datasets (training, validation). The usual split ratio followed is anywhere from (60% training, 40% validation) to (85% training, 15% validation). The validation data becomes the data points that the model will never see during training. The training data is the data on which the model is continuously trained.

[0045] The machine learning training process may include a training process. For machine learning models such as linear regression, logistic regression, decision trees, support vector machines, Xg-boost, etc., the training process may include (i) eliminating unimportant features (or data points) from the overall features (all numeric vectors). This is typically done using statistical methods such as correlation functions, information gain, regularization, etc.; and (ii) training based on selected important features. For neural network model (also known as deep learning) architectures, feature elimination is typically not required. Training of these models may include identifying different layers and layer sizes and defining an objective function based on purpose.

[0046] Embodiments of machine learning models can provide positive or negative impacts depending on the selected parameters. This can be implemented using binary classification models such as logistic regression, decision trees, neural networks, etc. Embodiments of machine learning models can provide impact analysis explanation text. The text is generated using a model (or the same model as above) that can generate text by analysis. These models follow different architectures for knowledge-aware systems such as LLM models. Some practical examples are Open AI ChatGPT, Bing Chat, Meta LLAMA2, Google Bard. These models typically follow a transformer architecture to generate text output based on the information provided as input.

[0047] Various models such as neural networks, decision trees, or support vector machines may be utilized to optimize accuracy and efficiency. The prediction system 214 may utilize a multi-language model to support multiple spoken languages, sign languages, and the like.

[0048] Prediction system 214 may be trained using data specific to the type of transportation system 101. For example, prediction system 214 may be trained based on the type of machine 111, whether elevator system 101 uses ropes or ropeless, number of floors, models of components (e.g., drive 111, controller 115, etc.), type of installation, etc. In this manner, prediction system 214 is trained using data relevant to a particular transportation system 101.

[0049] At 302, a user provides a problem description via the user device 202. The user may speak to provide a verbal problem description that is received by a microphone of the user device 202. The problem description may be entered via the user device 202 in other ways, such as a text description, a drop-down menu, etc. The user may locate the problem description using a search query entered via the user device 202.

[0050] At 304, the user device obtains current configuration parameters for the transportation system 101 from the controller 115. The current configuration parameters may include various controllable parameters of the elevator system 101, such as, but not limited to, elevator car speed limits, elevator car acceleration limits, door dwell time, lighting intensity, car load limits, etc. Other example configuration parameters include door reversal, door time closing protection, door time monitoring, double door operation, earthquake automatic recovery, emergency hospital service operation, and emergency power operation. Other examples of configuration parameters include (i) speed profile configuration (full speed, deceleration, etc.) that controls the elevator speed profile, (ii) canceling the hall call mode, in which the elevator can begin to ignore hall calls while only accepting car calls, (iii) disabling doors at specific landings, and (iv) door hold button time and timeout duration, which controls the amount of time a door can be forced to remain open by a passenger.

[0051] Each of these configuration parameters can be controlled by a subset of the feature's related parameters. For example, the configuration parameter Reopen Door Time is a subset of Door Reversal and controls the door dwell time in case of reopening. In another example, the configuration parameter Emergency Power Group Delay Time is a subset of Emergency Power Operation and controls the delay time in case of Emergency Power Operation.

[0052] At 306 , the problem description and current configuration parameters are sent to the prediction system 214 .

[0053] At 308, forecasting system 214 uses the problem description and current configuration parameters to determine one or more sets of updated configuration parameters that address the problem. Forecasting system 214 may perform an impact analysis on each set of updated configuration parameters. Impact analysis refers to what changes (one or more) the updated configuration parameters will have on transportation system 101.

[0054] At 310, prediction system 214 replies to user device 202 by sending a response to user device 202. The response includes one or more causes of the problem and / or one or more sets of updated configuration parameters corresponding to each cause of the problem. Prediction system 214 may also send an impact analysis of each set of updated configuration parameters to user device 202 (if performed). The one or more sets of updated configuration parameters may be prioritized by prediction system 214 such that a set of updated configuration parameters that is more likely to resolve the problem is presented with a higher priority than a set of updated configuration parameters that is less likely to resolve the problem.

[0055] At 312, the user selects a set of updated configuration parameters for verification via user device 202. If only one set of updated configuration parameters is provided from prediction system 214, the user selects the only set of updated configuration parameters.

[0056] At 314 , the selected set of updated configuration parameters is installed on the elevator controller 115 .

[0057] At 316, transportation system 101 operates in a trial run to verify that the selected set of updated configuration parameters has addressed the problem reported by the user. If the trial run is unsuccessful (e.g., the problem is not resolved), the process can return to 314, where the user can select a second set of updated configuration parameters for verification via user device 202.

[0058] The number of returns from 316 to 314 (e.g., the number of sets of updated configuration parameters that were attempted) can be limited by an upper limit. For example, if four sets of updated configuration parameters are attempted and none of the sets solve the problem, the process can end. At this point, the user can submit a revised problem description to the prediction system 214. In addition, the prediction system 214 can use the unsolvable problems as data for adaptive learning and updating the model(s) used by the prediction system 214.

[0059] If at 316 , the commissioning of transportation system 101 was successful (eg, the problem was resolved), flow proceeds to 318 , where the selected set of updated configuration parameters is saved in controller 115 .

[0060] At 320, the user may provide feedback to the predictive system 214 via the user device 202. The feedback may be positive or negative and is used by the predictive system 214 to improve the problem solving modeling.

[0061] At 322, the prediction system 214 can implement adaptive learning based on the feedback provided at 320. In this manner, the prediction system 214 learns from the problem solving results, improving accuracy and predictions over time.

[0062] Now turn to Figure 4 , a computer system 600 is generally shown according to an embodiment. The computer system 600 can be used to implement the prediction system 214. As described herein, the computer system 600 can be an electronic computer framework that includes and / or employs any number of computing devices and networks and combinations thereof that utilize various communication technologies. The computer system 600 can be easily scalable, extensible, and modular, with the ability to change to different services or reconfigure some features independently of other features. The computer system 600 can be, for example, a server, a desktop computer, a laptop computer, a tablet computer, or a smart phone. In some examples, the computer system 600 can be a cloud computing node. The computer system 600 can be described in the general context of computer system executable instructions such as program modules executed by the computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, etc. that perform specific tasks or implement specific abstract data types. The computer system 600 can be practiced in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located in both local and remote computer system storage media including memory storage devices.

[0063] like Figure 4 As shown in , the computer system 600 has one or more central processing units ((one or more) CPUs) 601a, 601b, 601c, etc. (collectively or generally referred to as (one or more) processors 601). The processor 601 can be a single-core processor, a multi-core processor, a computing cluster, or any number of other configurations. The processor 601, also known as a processing circuit, is coupled to a system memory 603 and various other components via a system bus 602. The system memory 603 may include a read-only memory (ROM) 604 and a random access memory (RAM) 605. The ROM 604 is coupled to the system bus 602 and may include a basic input / output system (BIOS) that controls certain basic functions of the computer system 600. The RAM is a read-write memory coupled to the system bus 602 for use by the processor 601. The system memory 603 provides temporary storage space for the operation of the instructions during operation. The system memory 603 may include a random access memory (RAM), a read-only memory, a flash memory, or any other suitable memory system.

[0064] Computer system 600 includes an input / output (I / O) adapter 606 and a communication adapter 607 coupled to system bus 602. I / O adapter 606 may be a small computer system interface (SCSI) adapter that communicates with hard disk 608 and / or any other similar components. I / O adapter 606 and hard disk 608 are collectively referred to herein as mass storage device 610.

[0065] Software 611 for execution on computer system 600 may be stored in mass storage device 610. Mass storage device 610 is an example of a tangible storage medium readable by processor 601, wherein software 611 is stored as instructions for execution by processor 601 to cause computer system 600 to operate, such as described below with respect to the various figures. Examples of computer program products and execution of such instructions are discussed in greater detail herein. Communications adapter 607 interconnects system bus 602 with network 612, which may be an external network, enabling computer system 600 to communicate with other such systems. In one embodiment, a portion of system memory 603 and mass storage device 610 together store an operating system, which may be any suitable operating system, such as z / OS or AIX operating systems from IBM Corporation, to coordinate Figure 4 The functions of the various components are shown in .

[0066] Additional input / output devices are shown connected to the system bus 602 via a display adapter 615 and an interface adapter 616. In one embodiment, adapters 606, 607, 615, and 616 may be connected to one or more I / O buses that are connected to the system bus 602 via an intermediate bus bridge (not shown). A display 619 (e.g., a screen or display monitor) is connected to the system bus 602 via a display adapter 615, which may include a graphics controller and a video controller to improve the performance of graphics-intensive applications. A keyboard 621, a mouse 622, a speaker 623, and the like may be interconnected to the system bus 602 via an interface adapter 616, which may include, for example, a super I / O chip that integrates multiple device adapters into a single integrated circuit. Suitable I / O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Thus, as Figure 4 As configured in, the computer system 600 includes processing capabilities in the form of a processor 601, as well as storage capabilities including system memory 603 and mass storage device 610, input components such as keyboard 621 and mouse 622, and output capabilities including speakers 623 and display 619.

[0067] In some embodiments, the communication adapter 607 can transmit data using any suitable interface or protocol, such as an Internet Small Computer System Interface, etc. The network 612 can be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, etc. An external computing device can be connected to the computer system 600 through the network 612. In some examples, the external computing device can be an external web server or a cloud computing node.

[0068] To understand, Figure 4 The block diagram is not intended to indicate that the computer system 600 is to include Figure 4 Rather, computer system 600 may include any suitable fewer or Figure 4 600 (e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). In addition, the embodiments described herein with respect to computer system 600 may be implemented with any suitable logic, wherein in various embodiments, logic as referred to herein may include any suitable hardware (e.g., a processor, an embedded controller, or an application specific integrated circuit, etc.), software (e.g., an application, etc.), firmware, or any suitable combination of hardware, software, and firmware.

[0069] As described above, embodiments may take the form of processor-implemented processes and apparatus for practicing those processes, such as a processor in prediction system 214. Embodiments may also take the form of computer program code containing instructions embodied in a tangible medium, such as a network cloud storage, an SD card, a flash drive, a floppy disk, a CD ROM, a hard drive, or any other computer-readable storage medium, wherein when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the embodiments. Embodiments may also take the form of computer program code, for example, whether stored in a storage medium, loaded into and / or executed by a computer, or transmitted through some transmission medium, such as transmitted by wire or cable, by optical fiber, or via electromagnetic radiation, wherein when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the embodiments. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.

[0070] The terms used herein are for the purpose of describing specific embodiments only 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 also be understood that the terms "comprises and / or comprising" when used in this specification specify the presence of stated 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, element components, and / or groups thereof.

[0071] Those skilled in the art will appreciate that various example embodiments are shown and described herein, each of which has certain features in a particular embodiment, but the disclosure is not limited thereto. On the contrary, the disclosure may be modified to incorporate any number of variations, alterations, substitutions, combinations, sub-combinations, or equivalent arrangements not previously described but commensurate with the scope of the disclosure. In addition, although various embodiments of the disclosure have been described, it is to be understood that aspects of the disclosure may include only some of the described embodiments. Therefore, the disclosure is not to be considered limited by the foregoing description, but only by the scope of the appended claims.

Claims

1. A method for providing assisted problem solving for a transportation system, the method comprising: receiving, at a forecasting system, a problem description of a problem at the transportation system and current configuration parameters of the transportation system; determining, at the prediction system, at least one set of updated configuration parameters directed at solving the problem; as well as A response including the at least one set of updated configuration parameters is sent from the prediction system to a user device.

2. The method of claim 1, further comprising training the prediction system using one or more of the following: engineering specifications, service records, and customer complaints.

3. The method of claim 1, wherein: The response includes an analysis of the impact of the at least one set of updated configuration parameters on the transportation system.

4. The method of claim 1, wherein: The response includes one or more reasons for the problem.

5. The method of claim 1, further comprising operating the transportation system in a trial run using the at least one set of updated configuration parameters. 6 . The method of claim 5 , further comprising saving the at least one set of updated configuration parameters when the commissioning indicates a successful resolution of the problem.

7. The method of claim 5, further comprising providing feedback to the prediction system after the trial run.

8. The method of claim 7, further comprising the prediction system performing adaptive learning based on the feedback.

9. The method of claim 1, wherein: The at least one set of updated configuration parameters comprises a plurality of sets of updated configuration parameters, the method further comprising: selecting one of the plurality of sets of updated configuration parameters as the selected set of updated configuration parameters; operating the transportation system in a trial run using the selected set of updated configuration parameters; and When the commissioning indicates a successful resolution of the problem, the selected set of updated configuration parameters is saved.

10. The method of claim 1, wherein: The transportation system is one of an elevator, an escalator and a moving walkway.

11. A system for providing assisted problem solving for a transportation system, the system comprising: A prediction system in communication with a user device via a network, the prediction system being configured to perform operations comprising: receiving, at the forecasting system, a problem description of a problem at the transportation system and current configuration parameters of the transportation system; At the predictive system, determining at least one set of updated configuration parameters that address the problem; and A response including the at least one set of updated configuration parameters is sent from the prediction system to a user device.

12. The system of claim 11, wherein: The operations further include training the prediction system using one or more of: engineering specifications, service records, and customer complaints.

13. The system of claim 11, wherein: The response includes an analysis of the impact of the at least one set of updated configuration parameters on the transportation system.

14. The system of claim 11, wherein: The response includes one or more reasons for the problem.

15. The system of claim 11, wherein: The transportation system is operated in a trial operation using the at least one set of updated configuration parameters.

16. The system of claim 15, wherein: The operations further include receiving feedback at the prediction system after the commissioning.

17. The system of claim 16, wherein: The operations further include the prediction system performing adaptive learning based on the feedback.

18. The system of claim 11, wherein: The transportation system is one of an elevator, an escalator and a moving walkway.

19. A computer program product embodied on a non-transitory computer readable storage medium, the computer program product comprising instructions for causing a processor to implement a process for providing assisted problem solving for a transportation system, the process comprising: receiving, at a forecasting system, a problem description of a problem at the transportation system and current configuration parameters of the transportation system; determining, at the prediction system, at least one set of updated configuration parameters directed at solving the problem; as well as A response including the at least one set of updated configuration parameters is sent from the prediction system to a user device.

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