Patent Publication Number: US-11040851-B2

Title: Elevator system passenger frustration reduction

Description:
BACKGROUND 
     The subject matter disclosed herein generally relates to elevator systems and, more particularly, to elevator systems configured to reduce passenger frustration. 
     Existing elevator systems attempt to provide improved service to passengers. Typically, elevator dispatching algorithms aim to reduce the average waiting time for passengers. Passengers, however, may have different preferences such as where to wait, with whom to travel, etc. Currently, passengers cannot provide this feedback, so elevator dispatching cannot be customized for a specific passenger based on their preferences. 
     SUMMARY 
     According to an embodiment, a method of controlling an elevator system incudes collecting one or more frustration indicators of a passenger during usage of the elevator system by the passenger; collecting one or more frustration factors; correlating the one or more frustration factors to the one or more frustration indicators to generate one or more preferences for the passenger; generating a profile for the passenger in response to the correlating, the profile including the one or more preferences; and controlling operation of the elevator system in response to the one or more preferences to reduce frustration of the passenger during interaction with the elevator system. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include ranking the one or more frustration factors from more frustrating to less frustrating. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include ranking the one or more preferences from most likely to reduce frustration to least likely to reduce frustration. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include controlling operation of the elevator system to reduce total frustration of a group of passengers during interaction with the elevator system. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include wherein the one or more frustration factors comprise internal frustration factors and external elevator factors. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include wherein the internal frustration factors comprise one or more of conditions within the elevator car, wait time for the elevator car, number of other passengers in the elevator car, number of stops during travel and overall travel time. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include wherein the external frustration factors include local traffic, local weather, time of day and day of week. 
     According to another embodiment, an elevator system includes an elevator car located within an elevator shaft; at least one sensor; an elevator controller arranged to control travel of the elevator car; and a computing system in communication with the at least one sensor and the elevator controller, wherein the computing system is configured to implement: collecting one or more frustration factors of a passenger; correlating the one or more frustration factors to the one or more frustration indicators to generate one or more preferences for the passenger; generating a profile for the passenger in response to the correlating, the profile including the one or more preferences; and controlling operation of the elevator system in response to the one or more preferences to reduce frustration of the passenger during interaction with the elevator system. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include wherein the controller is configured to implement ranking the one or more frustration factors from more frustrating to less frustrating. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include wherein the controller is configured to implement ranking the one or more preferences from most likely to reduce frustration to least likely to reduce frustration. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include wherein the controller is configured to implement controlling operation of the elevator system to reduce total frustration of a group of passengers during interaction with the elevator system. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include wherein the one or more frustration factors comprise internal frustration factors and external elevator factors. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include wherein the internal frustration factors comprise one or more of conditions within the elevator car, wait time for the elevator car, number of other passengers in the elevator car, number of stops during travel and overall travel time. 
     In addition to one or more of the features described herein, or as an alternative, further embodiments may include wherein the external frustration factors include local traffic, local weather, time of day and day of week. 
     According to another embodiment, a computer program product for controlling an elevator system, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to implement operations comprising: collecting one or more frustration indicators of a passenger during usage of the elevator system by the passenger; collecting one or more frustration factors; correlating the one or more frustration factors to the one or more frustration indicators to generate one or more preferences for the passenger; generating a profile for the passenger in response to the correlating, the profile including the one or more preferences; and controlling operation of the elevator system in response to the one or more preferences to reduce frustration of the passenger during interaction with the elevator system. 
     Technical effects of embodiments of the present disclosure include elevator systems configured to operate and/or adjust functionality in response to passenger frustration. Technical effects of the present disclosure also include elevator systems configured to learn frustration factors for passengers. 
     The foregoing features and elements may be combined in various combinations without exclusivity, unless expressly indicated otherwise. These features and elements as well as the operation thereof will become more apparent in light of the following description and the accompanying drawings. It should be understood, however, that the following description and drawings are intended to be illustrative and explanatory in nature and non-limiting. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements. 
         FIG. 1  depicts an elevator system in an example embodiment; 
         FIG. 2  depicts a computing system in an example embodiment; 
         FIG. 3  depicts data flows to and from the computing system in an example embodiment; 
         FIG. 4  depicts a process for learning preferences for a passenger to reduce passenger frustration in an example embodiment; 
         FIG. 5  depicts an elevator lobby in an example embodiment; and 
         FIG. 6  depicts a process for providing elevator service to reduce passenger frustration in an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a perspective view of an elevator system  101  including an elevator car  103 , a counterweight  105 , a roping  107 , a guide rail  109 , a machine  111 , a position encoder  113 , and an elevator controller  115 . The elevator car  103  and counterweight  105  are connected to each other by the roping  107 . The roping  107  may include or be configured as, for example, ropes, steel cables, and/or coated-steel belts. The counterweight  105  is configured to balance a load of the elevator car  103  and is configured to facilitate movement of the elevator car  103  concurrently and in an opposite direction with respect to the counterweight  105  within an elevator shaft  117  and along the guide rail  109 . 
     The roping  107  engages the machine  111 , which is part of an overhead structure of the elevator system  101 . The machine  111  is configured to control movement between the elevator car  103  and the counterweight  105 . The position encoder  113  may be mounted on an upper sheave of a speed-governor system  119  and may be configured to provide position signals related to a position of the elevator car  103  within the elevator shaft  117 . In other embodiments, the position encoder  113  may be directly mounted to a moving component of the machine  111 , or may be located in other positions and/or configurations as known in the art. 
     The elevator controller  115  is located, as shown, in a controller room  121  of the elevator shaft  117  and is configured to control the operation of the elevator system  101 , and particularly the elevator car  103 . For example, the elevator controller  115  may provide drive signals to the machine  111  to control the acceleration, deceleration, leveling, stopping, etc. of the elevator car  103 . The elevator controller  115  may also be configured to receive position signals from the position encoder  113 . When moving up or down within the elevator shaft  117  along guide rail  109 , the elevator car  103  may stop at one or more landings  125  as controlled by the elevator controller  115 . Although shown in a controller room  121 , those of skill in the art will appreciate that the elevator controller  115  can be located and/or configured in other locations or positions within the elevator system  101 . In some embodiments, the elevator controller  115  can be configured to control conditions within the elevator car  103 , including, but not limited to, lighting, display screens, music, spoken audio words, temperature in the car, etc. 
     The machine  111  may include a motor or similar driving mechanism. In accordance with embodiments of the 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, which, in combination with other components, is supplied to the motor. Although shown and described with a roping system, elevator systems that employ other methods and mechanisms of moving an elevator car within an elevator shaft may employ embodiments of the present disclosure.  FIG. 1  is merely a non-limiting example presented for illustrative and explanatory purposes. 
     Embodiments provided herein are directed to apparatuses, systems, and methods related to learning preferences for a passenger and providing elevator service in response to preferences for that passenger. Elevator systems of the present disclosure can include computing systems to generate instructions to take certain actions or responses, store certain operating mode information, store user profiles, enable control or communication, etc. 
     For example, referring now to  FIG. 2 , an exemplary computing system  200  that can be incorporated into elevator systems of the present disclosure is shown. The computing system  200  may be a stand-alone system (e.g., a server) or configured as part of the elevator controller  115  shown in  FIG. 1 . The computing system  200  includes a memory  202  which may store executable instructions and/or data. The executable instructions may be stored or organized in any manner and at any level of abstraction, such as in connection with one or more applications, processes, routines, procedures, methods, etc. In some embodiments, the computing system  200  may incorporate computing algorithms that are arranged to enable transmission of all collected sensing or video data to an elevator control system. In other embodiments, predetermined or relevant meta-data can be transmitted to the elevator control system, with such embodiments requiring less bandwidth, digital memory, and/or power. As an example, at least a portion of the instructions are shown in  FIG. 2  as being associated with a program  204 . 
     Further, as noted, the memory  202  may store data  206 . The data  206  may include profile or registration data, elevator car data, a device identifier, or any other type(s) of data as will be appreciated by those of skill in the art. The instructions stored in the memory  202  may be executed by one or more processors, such as a processor  208 . The processor  208  may be operative on the data  206 . 
     The processor  208  may be coupled to one or more input/output (I/O) devices  210 . In some embodiments, the I/O device(s)  210  may include one or more of a keyboard or keypad, a touchscreen or touch panel, a display screen, a microphone, a speaker, a mouse, a button, a remote control, a joystick, a printer, a telephone or mobile device (e.g., a smartphone), a sensor, video, etc. The I/O device(s)  210  may be configured to provide an interface to allow a user to interact with the computing system  200 . For example, the I/O device(s)  210  may support a graphical user interface (GUI) and/or voice-to-text capabilities. 
     The components of the computing system  200  may be operably and/or communicably connected by one or more buses. The computing system  200  may further include other features or components as known in the art. For example, the computing system  200  may include one or more transceivers and/or devices configured to transmit and/or receive information or data from sources external to the computing system  200 . For example, in some embodiments, the computing system  200  may be configured to receive information over a network (wired or wireless). The information received over the network may be stored in the memory  202  (e.g. as data  206 ) and/or may be processed and/or employed by one or more programs or applications (e.g., program  204 ). As shown, the computing system  200  includes a communications module  212  that can include various communications components for transmitting and/or receiving information and/or data over a variety of networks. 
     The computing system  200  may be used to execute or perform embodiments and/or processes described herein. For example, the computing system  200 , when configured as part of an elevator control system, may be used to receive commands and/or instructions, and may further be configured to control operation of and/or features of an elevator car. 
       FIG. 3  depicts data flows to and from the computing system  200  in an example embodiment. During a learning phase (described in further detail with respect to  FIG. 4 ), the computing system  200  receives frustration indicators for a passenger and frustration factors. The computing system  200  executes a learning module  220  that accumulates the frustration indicators and frustration factors and determines preferences for the passenger. The learning module  220  may be implemented by processor  208  executing a program  204  in memory  202  ( FIG. 2 ). The preferences for the passenger are then stored in a profile  222  for that passenger. The profiles  222  may be stored as data  206  in memory  202  ( FIG. 2 ). During a service phase, the preferences are accessed and used to control elevator service for that passenger by the control system  200  sending dispatching commands to the elevator controller  115 . 
       FIG. 4  depicts a learning phase process during which the computing system  20  learns preferences for a passenger to reduce passenger frustration, in an example embodiment. The process begins at  300  where the computing system  200  identifies a passenger accessing the elevator system  100 . The identity of the passenger may be determined using a variety of techniques including (a) identifying the passenger through a mobile device carried by the passenger, (b) identifying the passenger through cameras and face recognition software, (c) identifying the passenger based on biometric measurements such as a fingerprint (when pressing elevator call button) or iris recognition and (d) identifying the passenger by an identification medium (e.g., badge with RFID, magnetic stripe, etc.). 
     Once the passenger is identified, the computing system  200  begins collecting frustration indicators for the passenger. The frustration indicators may be collected for the entire period the passenger is interacting with the elevator system (e.g., waiting, boarding, traveling, de-boarding).  FIG. 5  depicts an elevator lobby in an example embodiment including systems for detecting passenger frustration indicators. A passenger device  510 , such as a smartphone or wearable, can monitor passenger physiological information (e.g., heart rate, body temperature, breathing, sweating, gait, etc.). When the passenger walks into a building or near an elevator system having embodiments described herein, detected passenger physiological state information and/or user data can be sent from the user device  510  to the computing system  200  (e.g., automatically, upon request from the elevator system, or pushed from the user device). 
     A sensor  520  may also detect frustration indicators for the passenger. For example, optical sensors, video cameras, microphones, thermal sensors, radar technologies, etc. can be used to detect the presence of passengers near or within an elevator car and can measure frustration indicators for the passenger. A camera, for example, may capture facial expressions/body language (e.g., discomfort, frequently looking at lantern, watch, frequently pressing call button, etc.). If the user device  510  includes a camera, the computing system  200  may evaluate images taken on the user device  510  to provide a frustration indicator for the passenger (e.g., angry facial expression in a selfie). A biometric sensor may obtain frustration indicators for the passenger through passenger interaction with the biometric sensor. In  FIG. 5 , the hall call panel  530  (or other fixture such as a destination call kiosk) includes a biometric sensor  540  that may detect or measure characteristics from the passenger&#39;s hand (e.g., heart rate, body temp, etc.). Biometric sensor  540  may also be used to identify the passenger (e.g., through fingerprint analysis). 
     Although the sensors in  FIG. 5  are shown in an elevator lobby, similar sensors may be used inside the elevator car  103  and at each landing of the elevator system  100 . Thus, frustration indicators for the passenger may be collected while the passenger is waiting for elevator service, while the passenger is traveling in the elevator car and when the passenger exits the elevator car. 
     Referring back to  FIG. 4 , at  304  the computing system  200  collects frustrating factors coincident with the passenger frustration indicators. The frustration factors may include elevator frustration factors, which refers to frustration factors experienced as a result of using the elevator system  100 . Examples of elevator frustration factors include wait time for an elevator car, number of other passengers in an elevator car, number of stops during travel, overall travel time (includes waiting time and travel time). Elevator frustration factors may also include conditions in the elevator car such as lighting, music, temperature, etc. The frustration factors may include external frustration factors, such as current local weather, current local traffic, day of week, time of day, etc. These external frustration factors may be obtained by the computing system  200  via a network connection to an external source of such factors (e.g., linking to a server providing local traffic and local weather via an RSS feed). 
     At  306 , the computing system  200  executes the learning module  220  to correlate the frustration indicators for the passenger with the frustration factors to determine how the frustration factors affect the frustration indicators for the passenger. The learning module  220  may use a variety of techniques to correlate the frustration indicators for the passenger with the frustration factors. For example, the learning module  220  may implement a Bayesian network to identify probabilistic relationships between the frustration indicators for the passenger with the frustration factors. Other methods used by learning module  220  may include neural networks, decision trees and association rules. The learning module  220  may also rank the frustration factors (or combinations of frustration factors) from most frustrating for the passenger to least frustrating to the passenger. The learning module  220  may also learn (e.g., enrich) a first passenger profile based on other passenger&#39;s frustration profiles that are similar to the first passenger profile by, for example, using recommender system methods such as content based, collaborative filtering, demography recommender or knowledge based recommender. 
     Once a correlation between the frustration indicators for the passenger and the frustration factors is determined, a user profile  222  for that passenger can be updated with preferences at  308 . These preferences are then used in controlling the elevator system in response to a call from that passenger. As a simple example, a passenger may exhibit high frustration indicators whenever there are four or more other passengers in the elevator car with the passenger or whenever elevator occupancy exceeds a percentage (e.g., 60%) of total capacity. From this information, the learning module  220  creates a preference in the user profile  222  for that passenger that the number of other passengers should be less than four, when possible. In a more complex example, the correlation between the frustration indicators for the passenger and the frustration factors may indicate that on Monday mornings the number of other passengers causes the most frustration, whereas on Friday afternoons the number of stops during transit causes the most frustration. The preferences may be considered proportionally related to the frustration factors, such that a frustration factor causing a high frustration indicator is associated with a strong preference. The preferences may also be ranked, from most likely to reduce frustration to least likely to reduce frustration, based upon the correlation between the frustration factors and frustration indicators for the passenger. 
     It is understood that the preferences in a user profile may include a wide variety of factors including reducing time waiting, reducing total travel time, reducing number of stops, reducing number of other passengers, elevator car lighting, elevator car music, elevator car temperature, etc. Embodiments are not limited to the preferences provided as examples in this disclosure. Similarly, the frustration indicators and frustration factors may include a wide variety of items. Embodiments are not limited to the frustration indicators and frustration factors provided as examples in this disclosure. 
     The learning phase of  FIG. 4  may be employed over a period of time such as weeks or months. At the beginning of the learning phase, a user&#39;s profile may be initialized with preferences that have been learned from other users and linked with their demographics, corporate affiliations, etc. The learning phase can also be reinitiated if the passenger appears to experience frustration levels that increase over time. 
       FIG. 6  depicts a service phase process during which the computing system  200  applies the preferences to reduce passenger frustration, in an example embodiment. The process begins at  600  where a passenger is identified. This may be performed using the same techniques as described with reference to block  300  of  FIG. 4 . If a passenger cannot be identified (e.g., a visitor to an office building), the elevator system will provide default elevator service and attempt to satisfy default preferences. 
     At  602 , the passenger&#39;s user profile is accessed to retrieve preferences for that passenger. As noted above, the preferences may be ranked from most likely to reduce frustration to least likely to reduce frustration. At  604 , the computing system  200  determines elevator dispatching commands for this passenger in order to satisfy the preferences, and therefore reduce (or minimize) passenger frustration. The elevator dispatching commands may include control of travel of the elevator car and control of conditions in the elevator car (e.g., lighting, sound, temperature, etc.). 
     There will be situations where multiple passengers have requested elevator service at the same time. In such situations, the computing system  200  obtains preferences from the user profiles of each passenger and determines the elevator dispatching commands that reduces the total frustration experienced by the group of passengers. This may be performed using data optimization techniques to locate a global minimum of total frustration for all passengers, rather than a local minimum for a single passenger. 
     At  606 , the computing system  200  provides the elevator dispatching commands to the elevator controller  115 . It is understood, as noted above, that the computing system  200  and elevator controller  115  may be implemented by the same device. The elevator controller  115  then assigns one or more elevator cars  103  to the one or more passengers who have made calls (e.g., hall or destination) to the elevator system. The elevator controller  115  may also control conditions within the elevator car  103 , including, but not limited to, lighting, display screens, music, spoken audio words, temperature in the car, etc. 
     Advantageously, embodiments of the present disclosure provide elevator systems that improve the passenger experience via personalized, automated, non-intrusive learning of individual preferences. 
     As described above, embodiments can be in the form of processor-implemented processes and devices for practicing those processes, such as a processor. Embodiments can also be in the form of computer program code containing instructions embodied in tangible media, such as network cloud storage, SD cards, flash drives, floppy diskettes, CD ROMs, hard drives, or any other computer-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes a device for practicing the embodiments. Embodiments can also be in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into an executed by a computer, the computer becomes a device for practicing the embodiments. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of 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, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof. 
     As described herein, in some embodiments various functions or acts may take place at a given location and/or in connection with the operation of one or more apparatuses, systems, or devices. For example, in some embodiments, a portion of a given function or act may be performed at a first device or location, and the remainder of the function or act may be performed at one or more additional devices or locations. Further, one of ordinary skill in the art will appreciate that the steps described in conjunction with the illustrative figures may be performed in other than the recited order, and that one or more steps illustrated may be optional. 
     Those of skill in the art will appreciate that various example embodiments are shown and described herein, each having certain features in the particular embodiments, but the present disclosure is not thus limited. Rather, the present disclosure can be modified to incorporate any number of variations, alterations, substitutions, combinations, sub-combinations, or equivalent arrangements not heretofore described, but which are commensurate with the scope of the present disclosure. Additionally, while various embodiments of the present disclosure have been described, it is to be understood that aspects of the present disclosure may include only some of the described embodiments. Accordingly, the present disclosure is not to be seen as limited by the foregoing description, but is only limited by the scope of the appended claims.