Lobby management system

Information

  • Patent Grant
  • 11775938
  • Patent Number
    11,775,938
  • Date Filed
    Friday, January 17, 2020
    4 years ago
  • Date Issued
    Tuesday, October 3, 2023
    a year ago
Abstract
One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to detect an individual who enters into a lobby of a building, retrieve context information corresponding to the individual, update a user profile of the individual to indicate the individual has arrived at the building, and control one or more access control devices to grant the individual access to the building based on the context information.
Description
BACKGROUND

A building management system (BMS) is, in general, a system of devices configured to control, monitor, and manage equipment in and/or around a building or building area. A BMS can include, for example, an HVAC system, a security system, a lighting system, a fire alerting system, and any other system that is capable of managing building functions or devices, or any combination thereof. As the number of BMS devices used in various sectors increases, the amount of data being produced and collected has been increasing exponentially. Accordingly, effective analysis and information management of a plethora of collected data is desired.


SUMMARY

One implementation of the present disclosure is one or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to detect an individual who enters into a lobby of a building, retrieve context information corresponding to the individual, update a user profile of the individual to indicate the individual has arrived at the building, and control one or more access control devices to grant the individual access to the building based on the context information.


In some embodiments, detecting the individual includes recognizing a physical characteristic of the individual. In some embodiments, the context information includes a security metric associated with the individual. In some embodiments, controlling the one or more access control devices is in response to comparing the security metric to a threshold. In some embodiments, the context information includes a schedule having events associated with the individual. In some embodiments, controlling the one or more access control devices further includes displaying directions to the individual to a location of a next event on the schedule of the individual. In some embodiments, the context information includes an interested person. In some embodiments, updating the user profile of the individual includes transmitting a notification to a device associated with the interested person indicating that the individual has arrived in the lobby. In some embodiments, the context information includes user preferences of the individual. In some embodiments, updating the user profile of the individual includes transmitting a notification to a food/beverage provider based on the user preferences of the individual.


Another implementation of the present disclosure is a method, including detecting an individual who enters into a lobby of a building, retrieving context information corresponding to the individual, wherein the context information includes a schedule having events associated with the individual, updating a user profile of the individual to indicate the individual has arrived at the building, and controlling one or more access control devices to grant the individual access to the building based on the context information, wherein controlling the one or more access control devices further includes displaying to the individual directions to a location of a next event on the schedule of the individual.


In some embodiments, detecting the individual includes recognizing a physical characteristic of the individual. In some embodiments, the context information includes a security metric associated with the individual. In some embodiments, controlling the one or more access control devices is in response to comparing the security metric to a threshold. In some embodiments, the context information includes an interested person. In some embodiments, updating the user profile of the individual includes transmitting a notification to a device associated with the interested person indicating that the individual has arrived in the lobby. In some embodiments, the context information includes user preferences of the individual. In some embodiments, updating the user profile of the individual includes transmitting a notification to a food/beverage provider based on the user preferences of the individual.


Another implementation of the present disclosure is a building management system (BMS), including one or more processing circuits and one or more computer readable storage media, the one or more computer readable storage media having instructions stored thereon that, when executed by the one or more processing circuits, cause the one or more processing circuits to detect an individual who enters into a lobby of a building, retrieve context information corresponding to the individual, wherein the context information includes an interested person, update a user profile of the individual to indicate the individual has arrived at the building, and control one or more access control devices to grant the individual access to the building based on the context information.


In some embodiments, updating the user profile of the individual includes transmitting a notification to a device associated with the interested person indicating that the individual has arrived in the lobby.


Another implementation of the present disclosure is one or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to detect a vehicle that enters into a parking lot, identify an individual associated with the vehicle, retrieve context information corresponding to the individual, transmit a notification to a lobby personnel indicating that the individual has arrived in the parking lot, and control one or more access control devices to grant the individual access to a building associated with the parking lot based on the context information.


In some embodiments, detecting the vehicle includes recognizing a license plate of the vehicle. In some embodiments, the context information includes a security metric associated with the individual. In some embodiments, controlling the one or more access control devices is in response to comparing the security metric to a threshold. In some embodiments, identifying the individual includes recognizing a physical characteristic of the individual. In some embodiments, the context information includes a schedule having events associated with the individual. In some embodiments, controlling the one or more access control devices further includes displaying to the individual directions to a location of a next event on the schedule of the individual. In some embodiments, the context information includes interested persons. In some embodiments, the system further transmits a notification to a device associated with at least one of the interested persons indicating that the individual has arrived in the parking lot.


Another implementation of the present disclosure is a method, including detecting a vehicle that enters into a parking lot, identifying an individual associated with the vehicle, retrieving context information corresponding to the individual, wherein the context information includes interested persons, transmitting a first notification to a lobby personnel indicating that the individual has arrived in the parking lot, and transmitting a second notification to a device associated with at least one of the interested persons indicating that the individual has arrived in the parking lot, and controlling one or more access control devices to grant the individual access to a building associated with the parking lot based on the context information.


In some embodiments, detecting the vehicle includes recognizing a license plate of the vehicle. In some embodiments, the context information includes a security metric associated with the individual. In some embodiments, controlling the one or more access control devices is in response to comparing the security metric to a threshold. In some embodiments, identifying the individual includes recognizing a physical characteristic of the individual. In some embodiments, the context information includes a schedule having events associated with the individual. In some embodiments, controlling the one or more access control devices further includes displaying to the individual directions to a location of a next event on the schedule of the individual.


Another implementation of the present disclosure is a building management system (BMS), including one or more processing circuits and one or more computer readable storage media, the one or more computer readable storage media having instructions stored thereon that, when executed by the one or more processing circuits, cause the one or more processing circuits to detect a vehicle that enters into a parking lot, identify an individual associated with the vehicle, retrieve context information corresponding to the individual, transmit a notification to a lobby personnel indicating that the individual has arrived in the parking lot, and control one or more access control devices to grant the individual access to a building associated with the parking lot based on the context information.


In some embodiments, detecting the vehicle includes recognizing a license plate of the vehicle. In some embodiments, the context information includes a security metric associated with the individual. In some embodiments, controlling the one or more access control devices is in response to comparing the security metric to a threshold.


Another implementation of the present disclosure is one or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to detect an individual who enters into a lobby of a building, retrieve context information corresponding to the individual, determine environmental characteristics of the lobby, and generate an action based on the context information and the environmental characteristics of the lobby.


In some embodiments, detecting the individual includes recognizing a physical characteristic of the individual. In some embodiments, determining environmental characteristics of the lobby include determining an occupancy metric associated with the lobby. In some embodiments, generating the action includes, in response to the occupancy metric exceeding a threshold, transmitting a notification to a device associated with the individual identifying an entrance for the building, wherein the entrance is located separately of the lobby. In some embodiments, the context information includes interested persons. In some embodiments, generating the action includes, in response to the occupancy metric exceeding a threshold, transmitting a notification to a device associated with at least one of the interested persons. In some embodiments, generating the action includes, in response to the occupancy metric exceeding a threshold, transmitting a notification to security personnel indicating a number of people in the lobby. In some embodiments, determining environmental characteristics of the lobby further includes measuring a temperature in the lobby. In some embodiments, generating the action includes controlling an HVAC system to change the temperature in the lobby based on the occupancy metric.


Another implementation of the present disclosure is a method, includes detecting an individual who enters into a lobby of a building, retrieving context information corresponding to the individual, determining environmental characteristics of the lobby, and generating an action based on the context information and the environmental characteristics of the lobby.


In some embodiments, detecting the individual includes recognizing a physical characteristic of the individual. In some embodiments, determining environmental characteristics of the lobby include determining an occupancy metric associated with the lobby. In some embodiments, generating the action includes, in response to the occupancy metric exceeding a threshold, transmitting a notification to a device associated with the individual identifying an entrance for the building, wherein the entrance is located separately of the lobby. In some embodiments, the context information includes interested persons. In some embodiments, generating the action includes, in response to the occupancy metric exceeding a threshold, transmitting a notification to a device associated with at least one of the interested persons. In some embodiments, generating the action includes, in response to the occupancy metric exceeding a threshold, transmitting a notification to security personnel indicating a number of people in the lobby. In some embodiments, determining environmental characteristics of the lobby further includes measuring a temperature in the lobby. In some embodiments, generating the action includes controlling an HVAC system to change the temperature in the lobby based on the occupancy metric.


Another implementation of the present disclosure is a building management system (BMS), including one or more processing circuits and one or more computer readable storage media, the one or more computer readable storage media having instructions stored thereon that, when executed by the one or more processing circuits, cause the one or more processing circuits to detect an individual who enters into a lobby of a building, retrieve context information corresponding to the individual, determine environmental characteristics of the lobby, and generate an action based on the context information and the environmental characteristics of the lobby.


In some embodiments, determining environmental characteristics of the lobby include determining an occupancy metric associated with the lobby, and wherein generating the action includes, in response to the occupancy metric exceeding a threshold, transmitting a notification to a device associated with the individual identifying an entrance for the building, wherein the entrance is located separately of the lobby.





BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects and features of the present disclosure will become more apparent to those skilled in the art from the following detailed description of the example embodiments with reference to the accompanying drawings.



FIG. 1A is a block diagram of a smart building environment, according to an exemplary embodiment.



FIG. 1B is another block diagram of the smart building environment of FIG. 1A, according to an exemplary embodiment.



FIG. 2 is a block diagram of a building data platform associated with the smart building environment of FIGS. 1A-1B, according to an exemplary embodiment.



FIG. 3A is a block diagram of an entity graph, according to an exemplary embodiment.



FIG. 3B is another block diagram of the entity graph of FIG. 3A, according to an exemplary embodiment.



FIG. 4A is a block diagram of a lobby management system, according to an exemplary embodiment.



FIG. 4B is a node graph illustrating connections between features and components of the lobby management system of FIG. 4A, according to an exemplary embodiment.



FIG. 5 is a flow diagram of a method of performing actions in response to the arrival of an individual, according to an exemplary embodiment.



FIG. 6 is a flow diagram of a method of generating an action based on an environmental characteristic of a lobby, according to an exemplary embodiment.



FIG. 7 is a perspective view of a lobby implementing the lobby management system of FIG. 4A, according to an exemplary embodiment.





DETAILED DESCRIPTION

Referring generally to the FIGURES, described herein are systems and methods of a lobby management system. A lobby management system may include a smart lobby and a smart lobby system. The smart lobby may include sensors configured to generate data for identifying individuals. The smart lobby may further include access control devices and/or digital displays. The smart lobby may be communicably coupled to the smart lobby system. The smart lobby system may identify, based on the data from the sensors, an individual, traverse an entity graph data structure forming a digital twin of the individual to retrieve context information corresponding to the individual, and take actions based on the retrieved context information. As a further example, the smart lobby system may detect an individual who enters into a lobby, retrieve context information corresponding to the individual, update a digital twin of the individual to indicate that the individual has arrived at the lobby, and control one or more access control devices based on the context information. As a further example, the smart lobby system may detect an individual who enters into a lobby, retrieve context information corresponding to the individual, determine environmental characteristics associated with the lobby, and generate an action based on the context information and the environmental characteristics of the lobby. In some embodiments, the smart lobby system may send a notification to a mobile device associated with the individual to direct them to a different entrance.


Hereinafter, example embodiments will be described in more detail with reference to the accompanying drawings. Referring now to FIGS. 1A-1B, a block diagram of a smart building environment 100 is shown, according to an exemplary embodiment. Smart building environment 100 is shown to include cloud building management platform 140. Cloud building management platform 140 may be configured to collect information from a variety of different data sources. Cloud management platform 140 may create digital representations, referred to as “digital twins,” of physical spaces, equipment, people, and/or events based on the collected information. In various embodiments, the digital representations are stored in an entity graph. In brief overview, an entity graph is a data structure representing entities (e.g., spaces, equipment, people, events, etc.) and relationships between the entities. In various embodiments, the entity graph data structure facilitates advanced artificial intelligence and machine learning associated with the entities. In various embodiments, entities within the entity graph data structure include or are associated with “agents,” or software entities configured to take actions with respect to the digital twins/real world entities with which they are associated. In some implementations, the agents may be configured to implement artificial intelligence/machine learning methodologies. The agents may be configured to facilitate communication and collection of information between the variety of different data sources. Each of the data sources may be implemented as, include, or otherwise use respective agents for facilitating communication amongst or between the data sources and cloud building management platform 140. The agents of cloud building management platform 140 and data sources may be configured to communicate using defined channels across which the agents may exchange information, messages, data, etc. amongst each other. In some examples, channels may be defined for particular spaces, subspaces, control loops, groups of equipment, people, buildings or groups of buildings, etc. In some implementations, agents may communicate by publishing messages to particular channels and subscribing to messages on particular channels and/or published by particular other agents/types of agents. In various embodiments, the data sources include buildings. For example, cloud building management platform 140 may interact with a number of buildings, each of which may include an agent (or a group of agents corresponding to various building subsystems within the respective building), to receive information. Hence, cloud building management platform 140 and the data sources may together form a network of agents to facilitate artificially intelligent exchange and communication of information across various channels. In some embodiments, one or more device(s), component(s), space(s) (and sets of devices, components, spaces) within cloud building management platform 140 may include a respective agent dedicated to perform various tasks associated therewith. The agents may therefore be dedicated for performing separate functions or tasks.


In various embodiments, cloud building management platform 140 collects data from buildings 10. For example, cloud building management platform 140 may collect data from buildings 10 such as a school, a hospital, a factory, an office building, and/or the like. It should be understood that the present disclosure is not limited to the number or types of buildings 10 shown in FIG. 1B. As new devices/components/spaces/buildings/events/control loops are added or otherwise incorporated into smart building environment 100, new digital representations (and associated agents, etc.) may be dynamically generated and incorporated into the entity graph data structure. Various examples of agents and corresponding networking may be found in U.S. patent application Ser. No. 15/934,593, filed Mar. 23, 2018, and titled “Building Management System with Dynamic Channel Communication”, P.C.T. Application No. PCT/US2018/037,589, filed Jun. 14, 2018, and titled “Building Management System with Artificial Intelligence for Unified Agent Based Control of Building Subsystems,” and U.S. patent application Ser. No. 16/036,685, filed Jul. 16, 2018, and titled “Systems and Methods for Agent Based Building Simulation for Optimal Control”, the contents of each of which are incorporated herein by reference.


Buildings 10 may include entities 12. Entities 12 may include spaces, equipment, people, and/or events. In some embodiments, entities 12 include spaces such as floors, rooms, zones, campuses, buildings, and the like. In some embodiments, entities 12 include people such as employees, visitors, pedestrians, staff, and the like. In some embodiments, entities 12 include equipment such as inventory, assets, furniture, vehicles, building components, devices, and the like. For example, entities 12 may include devices such as internet of things (IoT) devices. IoT devices may include any of a variety of physical devices, sensors, actuators, electronics, vehicles, home appliances, and/or other items capable of communicating data over an electronic network (e.g., smart lights, smart appliances, smart home hub devices, etc.). In some embodiments, entities 12 include events such as meetings, fault indications, alarms, and the like. In various embodiments, cloud building management platform 140 receives information associated with buildings 10 and/or entities 12 and generates entity graph 170 based on the received information. Entity graph 170 may include digital twins that are digital representations of real world spaces, equipment, people, events, and/or the like. Entity graph 170 is described in greater detail below with reference to FIG. 3A-3B.


Smart building environment 100 may include building management system (BMS) 102. In various embodiments, BMS 102 communicates with cloud building management platform 140 to facilitate management and control of buildings 10 and/or the various operations described herein. BMS 102 may be configured to control, monitor, and/or manage equipment in or around a building or building area (e.g., such as buildings 10, etc.). For example, BMS 102 may include a HVAC system, a security system, a lighting system, a fire alerting system, and any other system that is capable of managing building functions or devices, or any combination thereof. Further, each of the systems may include sensors and other devices (e.g., IoT devices) for the proper operation, maintenance, monitoring, and the like of the respective systems. In some embodiments, each of buildings 10 is associated with a BMS 102. Additionally or alternatively, a single BMS 102 may manage multiple buildings 10. For example, a first BMS 102 may manage a first building 10, a second BMS 102 may manage a second building 10, and a third BMS 102 may manage the first and second buildings 10 (e.g., via the first and second BMS 102, in a master-slave configuration, etc.), as well as a third building 10. In various embodiments, BMS 102 communicates with building subsystems 120.


Building subsystems 120 may include fire safety subsystem 122, lift/escalators subsystem 124, building electrical subsystem 126, information communication technology (ICT) subsystem 128, security subsystem 130, HVAC subsystem 132, and/or lighting subsystem 134. In various embodiments, building subsystems 120 include fewer, additional, or alternative subsystems. For example, building subsystems 120 may additionally or alternatively include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable equipment and/or sensors to monitor or control a building 10. In some embodiment each of buildings 10 includes building subsystems 120. Additionally or alternatively, multiple buildings 10 may share at least some of building subsystems 120.


Each of building subsystems 120 may include any number of devices (e.g., IoT devices), sensors, controllers, and connections to facilitate functions and control activities. For example, HVAC subsystem 132 may include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature, humidity, airflow, or other variable conditions within buildings 10. Lighting subsystem 134 may include any number of light fixtures, ballasts, lighting sensors, dimmers, or other devices configured to controllably adjust the amount of light provided to a building space. Security subsystem 130 may include occupancy sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices and servers, or other security-related devices.


Cloud building management platform 140 and/or BMS 102 may interact with a variety of external systems. For example, cloud building management platform 140 may interact with remote systems and applications 30, client devices 40, and/or third party services 50. In various embodiments, systems and/or components of smart building environment 100 are configured to communicate using network 20. Network 20 may include hardware, software, or any combination thereof.


BMS 102 is shown to include communications interface 104 and processing circuit 106. Communications interface 104 may facilitate communications between BMS 102 and external systems/applications (e.g., cloud building management platform 140, remote systems and applications 30, client devices 40, third party services 50, building subsystems 120, etc.). Communications interface 104 may be or include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications within smart building environment 100 and/or with other external systems or devices. In various embodiments, communications via communications interface 104 is direct (e.g., local wired or wireless communications). Additionally or alternatively, communications via communications interface 104 may be via network 20 (e.g., a WAN, the Internet, a cellular network, etc.). For example, cloud building management platform 140 may communicate with BMS 102 using a wired connection and may communicate with client devices 40 (e.g., via BMS 102, etc.) using a cellular connection (e.g., a 4G or 5G access point/small cell base station, etc.). As a further example, communications interface 104 may include an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. As a further example, communications interface 104 may include a Wi-Fi transceiver for communicating via a wireless communications network. As yet a further example, communications interface 104 may include cellular or mobile phone communications transceivers.


Processing circuit 106 may include processor 108 and memory 110. Processing circuit 106 may be communicably connected to communications interface 104 such that processing circuit 106 and the various components thereof can send and receive data via communications interface 104. Processor 108 may be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components.


Memory 110 (e.g., memory, memory unit, storage device, etc.) may include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present application. Memory 110 may be or include volatile memory or non-volatile memory. Memory 110 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present application. According to some embodiments, memory 110 is communicably connected to processor 108 via processing circuit 106 and includes computer code for executing (e.g., by processing circuit 106 and/or processor 108) one or more of the operations described herein.


In some embodiments, BMS 102 and/or cloud building management platform 140 are implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments BMS 102 and/or cloud building management platform 140 are distributed across multiple servers or computers (e.g., that can exist in distributed locations). In some embodiments, functions of BMS 102 and/or cloud building management platform 140 are implemented as agents. For example, BMS 102 may include a fault detection agent configured to analyze building data and detect faults associated with building components.


Memory 110 may include applications circuit 112 that may include building management application(s) 114. Building management application(s) 114 may include various systems to monitor and/or control specific processes/events within buildings 10. For example, building management application(s) 114 may include automated measurement and validation (AM&V), demand response (DR), fault detection and diagnostics (FDD), integrated control systems, and/or a building subsystem integration system. Building management application(s) 114 may be configured to receive inputs from building subsystems 120 and/or other data sources, determine improved and/or optimal control actions for building subsystems 120 based on the inputs, generate control signals based on the improved and/or optimal control actions, and provide the generated control signals to building subsystems 120.


Cloud building management platform 140 is shown to include processing circuit 142 having processor 144 and memory 146. In some embodiments, cloud building management platform 140 includes multiple processing circuits 142 each having one or more processors 144 and/or memories 146. Processor 144 may be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. Processor 144 may be configured to execute computer code or instructions stored in memory 146 or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.).


Memory 146 may include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for completing and/or facilitating the various processes described in the present disclosure. Memory 146 may include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. Memory 146 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. According to some embodiments, memory 146 is communicably connected to processor 144 via processing circuit 142 and includes computer code for executing (e.g., by processing circuit 142 and/or processor 144) one or more of the operations described herein.


Memory 146 may include data management circuit 148, entity graph circuit 150, analytics circuit 152, event management circuit 154, applications circuit 156, and/or user interface circuit 158. Data management circuit 148 may be configured to collect, manage, and/or retrieve data. In various embodiments, data management circuit 148 receives data samples from buildings 10 (e.g., via BMS 102, directly, etc.) and stores the data samples in structured storage. For example, the data samples may include data values for various data points. The data values may be measured and/or calculated values, depending on the type of data point. For example, a data point received from a temperature sensor may include a measured data value indicating a temperature measured by the temperature sensor. Data management circuit 148 may receive data samples from systems, components, and/or devices (e.g., IoT devices, sensors, etc.) within smart building environment 100 (e.g., remote systems and applications 30, client devices 40, third party services 50, BMS 102, building subsystems 120, etc.) and/or from external systems (e.g., the Internet, etc.). For example, data management circuit 148 may receive timeseries data from an occupancy sensor associated with one of buildings 10 and facilitate storage of the timeseries data in structured storage (e.g., in entity graph 170, etc.). As a further example, data management circuit 148 may receive an electronic calendar event (e.g., a meeting invitation, etc.) from one of client devices 40 and facilitate storage of the electronic calendar event in structure storage (e.g., in entity graph 170, etc.). In some embodiments, data management circuit 148 uses or retrieves an entity graph (e.g., via entity graph circuit 150, etc.) when organizing received data.


Entity graph circuit 150 may be configured to manage entity graph 170. In various embodiments, entity graph circuit 150 registers and manages various buildings (e.g., building 10, etc.), spaces, persons, subsystems (e.g., building subsystems 120, etc.), devices (e.g., IoT devices, etc.), events, and/or other entities in cloud building management platform 140. As described above, an entity may be any person, place, space, physical object, equipment, or the like. Further, an entity may be any event, data point, record structure, or the like. Entities and entity graph 170 are described in detail below with reference to FIGS. 3A-3B.


Analytics circuit 152 may be configured to analyze data to generate results. For example, analytics circuit 152 may analyze sensor data (e.g., weight measurements, image data, audio data, etc.) from a building lobby to identify a user. As a further example, analytics circuit 152 may apply fault detection rules to timeseries data from an HVAC system to detect a fault associated with the HVAC system. In various embodiments, analytics circuit 152 performs operations on information stored in entity graph 170. For example, analytics circuit 152 may traverse entity graph 170 to retrieve context information (e.g., energy usage, event activity, occupancy sensor data, HVAC control schedules, etc.) associated with one of buildings 10, and analyze the context information to determine a user schedule associated with the building (e.g., when the building is most heavily in use, etc.).


Event management circuit 154 may be configured to generate actions. For example, event management circuit 154 may receive event data from building subsystems 120 (e.g., a security alarm, etc.), and generate a response based on the event data (e.g., cause BMS 102 to sound an alarm, etc.). In various embodiments, event management circuit 154 generates actions dynamically. For example, event management circuit 154 may include artificially intelligent agents configured to generate actions in real-time based on received input. For example, event management circuit 154 may include an AI agent that dynamically generates a notification to an interested party in response to receiving an indication of an identified individual. As a further example, event management circuit 154 may receive a prediction from analytics circuit 152 that a building component is about to enter a fault state and may dynamically generate a work order ticket for the building component in response to the received prediction.


Applications circuit 156 may be configured to facilitate a variety of applications associated with cloud building management platform 140. For example, applications circuit 156 may facilitate a smart messaging system, a personal comfort system, a health and wellness system, a smart parking lot system, a smart signage system, a smart lobby system, a smart meeting room system, an employee productivity system, and/or the like. In various embodiments, applications circuit 156 facilitates operation of various systems that integrate with smart building environment 100. For example, applications circuit 156 may facilitate a FDD system that receives data from buildings 10 and generates fault indications associated with buildings 10.


User interface 158 may be configured to facilitate user interaction with cloud building management platform 140 and/or BMS 102. For example, a user may update personalized preferences associated with operation of cloud building management platform 140 via user interface 158. In some embodiments, user interface 158 facilitates dynamic feedback (e.g., a natural user interface, etc). For example, user interface 158 may facilitate chatbot interaction, voice commands, user authentication, biometric feedback, or the like.


Referring now to FIG. 2, a building data platform 200 associated with the smart building environment 100 is shown, according to an exemplary embodiment. In various embodiments, cloud building management platform 140 implements the architecture of building data platform 200. Building data platform 200 is shown to include various layers 240. For example, layers 240 may include an interaction layer, an experience and outcome service layer, a policy and workflow management layer, data collaboration layer, entity graph layer, and/or a system of system integration and data service layer. In various embodiments, building data platform 200 includes interface(s) 202. For example, interface(s) 202 may include a mobile phone application, a natural user interface (e.g., voice recognition, chatbot services, text recognition, etc.), a browser application, a signage system, and/or the like. Interface(s) 202 may facilitate human-to-machine interaction, information visualization, and user experience functions.


In various embodiments, building data platform 200 includes service(s) 204. Service(s) 204 may include various user deliverables (e.g., outcomes, experiences, etc.) facilitated by building data platform 200. For example, service(s) 204 may include meeting scheduling, energy management, building supplies replenishment, lobby management (e.g., tracking a number of individuals in a building lobby and responding based on the number of individuals, etc.), facility management, productivity features (e.g., measuring and reporting on employee productivity, generating productivity suggestions, etc.), restroom management (e.g., monitoring a cleanliness of building restrooms, etc.), personal comfort management (e.g., adjusting building parameters based on occupant comfort preferences, etc.), employee engagement features (e.g., monitoring and reporting on employee engagement, generating engagement suggestions, etc.), parking management (e.g., dynamically assigning parking spaces, etc.), location services (e.g., generating actions based on users' locations, etc.), health and wellness features (e.g., monitoring and reporting on employee health and wellness, generating health and wellness suggestions, etc.), smart security (e.g., dynamically identifying individuals within a building, monitoring security parameters associated with a building, etc.), branding features (e.g., dynamic digital signage updating based on an identity of a viewer, etc.), and/or utility features (e.g., monitoring and reporting on building utility usage, generating suggestions to reduce utility consumption and/or cost, etc.). In various embodiments, service(s) 204 generate a virtual view of data from data collaboration, business workflows, and downstream sub-systems (e.g., sensors, actuators, etc.).


In various embodiments, building data platform 200 includes event processing 206. Event processing 206 may facilitate generating actions based on received data. For example, event processing 206 may receive an indication of an event within buildings 10, retrieve information associated with the event, and trigger a set of predefined workflows to perform management policies. In various embodiments, event processing 206 includes complex event processing and/or a business workflow processing engine (e.g., a rules engine, etc.) integrated with messaging and data models (e.g., event data models, etc.).


In various embodiments, building data platform 200 includes data source(s) 208. For example, data source(s) 208 may include data associated with people, places, assets, and/or the like. In various embodiments, building data platform 200 interacts with digital twins included in entity graph 170. For example, building data platform 200 may project a digital twin into a virtual data view to facilitate service(s) 204. Data source(s) 208 may manage a database view of digital representation of people, places and assets. In various embodiments, data source(s) 208 represent heterogeneous source data schema as an open source common data model (e.g., a Brick Schema/extensions, etc.).


In various embodiments, entity graph layer 240 includes digital twin 210 and context information 212. Digital twin 210 is a digital representation of spaces, assets, people, events, and/or anything associated with a building or operation thereof. In various embodiments, digital twin 210 is modeled in entity graph 170. In various embodiments, digital twins 210 include an active compute process. For example, a digital twin 210 may communicate with other digital twins 210, and to sense, predict and acts. In various embodiments, digital twin 210 is generated dynamically. For example, a digital twin 210 corresponding to a conference room may update its status by looking at occupancy sensors or an electronic calendar (e.g., to turn its status “available” if there is no show, etc.). In various embodiments, digital twin 210 and/or entity graph 170 include context information 212. Context information 212 may include real-time data and a historical record of each system in the environment (e.g., campus, building, facility, space, etc.). Context information 212 may be stored in entity graph 170. In various embodiments, context information 212 facilitates flexible data modeling for advanced analytics and AI application in scenarios that model highly interconnected entities.


In various embodiments, building data platform 200 includes data management 214 and/or operation(s) 216. Data management 214 may manage, retrieve, and transmit data to various systems. For example, data management 214 may retrieve and transmit data integration protocols to OT sub-systems. Operation(s) 216 may include data storage attribution, schema management, smart entity management, information integration, schema transformation, intelligent messaging, batch analytics, stream analysis, and/or device assurance.


In various embodiments, building data platform 200 includes administration and monitoring 220 and/or identity and security 230. Administration and monitoring 220 may facilitate various administrative functions and/or operations. For example, an administrator may view memory allocation analytics associated with building data platform 200 (e.g., how much memory does entity graph 170 occupy, etc.). Identity and security 230 may facilitate various security features. For example, identity and security 230 may encrypt personally identifiable information (PII) included in digital twin 210.


Referring now to FIGS. 3A-3B, an entity graph 300 is shown in greater detail, according to an exemplary embodiment. In brief overview, entity graphs such as entity graph 170 and/or entity graph 300 are structured data stored in memory (e.g., a database, memory 146, etc.). Entity graphs such as entity graph 300 and/or entity graph 170 may include digital twins. Digital twins may be digital representations of real world spaces, equipment, people, and/or events. In various embodiments, digital twins represent buildings, building equipment, people associated with buildings, and/or events associated with buildings (e.g., buildings 10, etc.). An entity graph may include nodes and edges, where each node of the entity graph represents an entity and each edge is directed (e.g., from a first node to a second node) and represents a relationship between entities (e.g., indicates that the entity represented by the first node has a particular relationship with the entity represented by the second node). For example, an entity graph may be used to represent a digital twin of a person.


Entities can be things and/or concepts related to spaces, people, and/or asset. For example, the entities could be “B7F4 North”, “Air Handling Unit,” and/or “meeting room.” The nodes can represent nouns while the edges can represent verbs. For example, the edges can be “isA,” “hasPart,” and/or “feeds.” In various embodiments, the edges represent relationships. While the nodes represent the building and its components, the edges describe how the building operates. The nodes and edges together create a digital twin of a particular building. In some embodiments, the entities include properties or attributes describing the entities (e.g., a thermostat may have a particular model number attribute). The components of the entity graph form large networks that encode semantic information for a building.


The entity graph is configured to enable flexible data modeling for advanced analytics, control, and/or artificial intelligence applications, in some embodiments. These applications may require, or benefit from information modeling including interconnected entities. Other data modeling techniques based on a table, a hierarchy, a document, and/or a relational database may not be applicable. The entity graph can be a foundational knowledge management layer to support other higher level applications, which can be, complex root cause, impact analysis, building powerful recommendation engines, product taxonomy information services, etc. Such a multilayer system, a system of system topologies, can benefit from an underlying entity graph.


The entity graph can be a data contextualization layer for all traditional and/or artificial intelligence applications. The entity graph can be configured to capture evidence that can be used to attribute the strengths of entity relationships within the entity graph, providing the applications which utilize the entity graph with context of the systems they are operating. Without context (e.g., who the user is, what the user is looking for, what the target of a user request is, e.g., find a meeting room, increase a temperature in my office) these applications may never reach their full potential. Furthermore, the entity graph provides a native data structure for constructing question and answer type systems, e.g., a chatbot, that can leverage and understand intent.


The entity graph may not be a configuration database but may be a dynamic representation of a space, person, event, and the like. The entity graph can include operational data from entities which it represents, e.g., sensors, actuators, card access systems, occupancy of a particular space, thermodynamics of the space as a result of actuation, etc. The entity graph can be configured to continually, and/or periodically, ingest new data of the space and thus the entity graph can represent a near real-time status of cyber-physical entities and their inter-relationships. For this reason, artificial intelligence can be configured to introduce a virtual entity and new semantic relationships among entities, in some embodiments.


The entity graph is configured to facilitate adaptive controls, in some embodiments. The entity graph can be configured to adapt and learn over time. The entity graph can be configured to enable dynamic relationships between building information and other facility and enterprise systems to create new insights and drive new optimization capabilities for artificial intelligence systems. As relationships can be learned over time for the entity graph, the artificial intelligence systems and also learn overtime based on the entity graph. Entity graphs (e.g., space graphs, etc.) are described in greater detail with reference to U.S. patent application Ser. No. 16/260,078, filed on Jan. 28, 2019, the entire disclosure of which is incorporated by reference herein.


Entity graph 300 includes entities 302-358 (stored as nodes within entity graph 300) describing spaces, equipment, events, and people (e.g., business employees, etc.). In various embodiments, entities 302-358 are associated with or otherwise include agents (e.g., agents may be assigned to/associated with entities, etc.). Additionally or alternatively, agents may be represented as nodes in entity graph 300 (e.g., agent entities, etc.). Furthermore, relationships are shown between entities 302-358 directionally describing relationships between two of entities 302-358 (stored as edges within entity graph 300). In various embodiments, cloud building management platform 140 may traverse entity graph 300 to retrieve a description of what types of actions to take for a certain device, what the current status of a room is (e.g., occupied or unoccupied), etc.


As an example, entity graph 300 illustrates an office space called “B7F5 North” of a building. A smart TV referred to as “Smart TV 001” has a directional relationship to the space referred to as “B7F5 North.” The relationship may be an edge “hasLocation” indicating that the device (e.g., the smart TV represented by entity 324) has a location (e.g., the space represented by entity 302). Furthermore, a second edge “contains” from entity 302 to entity 324 indicates that the location (e.g., the space represented by entity 302) includes the device (e.g., the smart TV represented by entity 324). In some embodiments, entity graph circuit 150 generates the nodes of entity graph 300 from various data sources including a building automation system, a security system, a fire alarm, human resources system, and/or building information model (BIM) files (e.g., through an entity name matching process, etc.). Furthermore, semantic relationships may be extracted from the building information by entity graph circuit 150. In some embodiments, only a single relationship exists between entities. In some embodiments, nodes and edges are determined dynamically as building data that is received and ingested into entity graph 300. For example, cloud building management platform 140 is configured to identify a door lock and card reader and generate a number of nodes and edges in entity graph 300 representing the card reader controller operation of the door lock.


Smart Lobby System


Speaking now generally, described herein are systems and methods of a smart lobby system. The smart lobby described herein may facilitate individual user experiences. For example, a smart lobby may identify an individual and/or a group of individuals and react based on the identified individual and/or group of individuals. In some embodiments, a smart lobby system may identify an individual, retrieve a schedule associated with the individual, and grant the individual access to a location within a building based on the schedule. In various embodiments, the smart lobby system described herein may at least partially integrate with an external system (e.g., BMS 102, cloud building management platform 140, entity graph 170, etc.). For example, a smart lobby may reference entity graph 170 to facilitate user identification. In various embodiments, the smart lobby system described herein facilitates frictionless access to a building. Additionally or alternatively, the smart lobby system described herein may facilitate dynamic actions based on occupancy conditions. For example, a smart lobby may dynamically redirect individuals to different building entrances based on an occupancy metric (e.g., a number of people at an entrance, individuals waiting in a long line at an entrance may be dynamically redirected to an entrance with a shorter line, etc.).


As a non-limiting example, a smart lobby may detect the presence of an individual in a lobby. The smart lobby may capture an image of the individual and perform facial recognition to determine an identity of the individual. Using the identity of the individual, the smart lobby may retrieve context information associated with the individual. For example, the smart lobby may retrieve access rights and a schedule of the individual. The smart lobby may determine, based on the schedule of the individual, that the individual has an upcoming meeting. Furthermore, the smart lobby may determine, based on the access rights of the individual, that the individual is authorized to enter portions of the building to access a location associated with the upcoming meeting. The smart lobby may control access control devices to grant the individual access to the location of the upcoming meeting. Additionally or alternatively, the smart lobby may display directions to the individual describing a route to the location of the upcoming meeting. In some embodiments, the smart lobby may display turn-by-turn directions on digital signage located throughout the lobby and/or building and/or may provide such directions fully or partially to a device of the individual (e.g., the individual's smartphone).


As another non-limiting example, a smart lobby may receive an indication from a smart parking system. For example, the smart lobby may receive an indication from a smart parking system that an individual has arrived at a parking lot associated with a building. The smart lobby may retrieve context information associated with the individual. For example, the smart lobby may retrieve an organizational role (e.g., a seniority, a title, an organizational position, etc.) associated with the individual. Additionally or alternatively, the smart lobby may determine environmental characteristics associated with various entrances of the building. For example, the smart lobby may determine how many people are waiting in line at each of the various entrances. The smart lobby may determine, based on the organizational role of the individual, that the individual is a very-important person (VIP). Furthermore, the smart lobby may determine, based on the environmental characteristics of the various entrances, that a main lobby has a long wait time (e.g., an estimated or measured wait time above a particular time threshold). The smart lobby may analyze the context information and notify an employee escort that the individual has arrived at the parking lot. Furthermore, the smart lobby may display personalized directions to the individual to direct the individual to an entrance having a short wait time and/or to an entrance where the employee escort is waiting. For example, the smart lobby may display the personalized message and directions on digital signage located in the parking lot.


Referring now to FIGS. 4A-4B, a smart lobby management system including smart lobby system 1100 is shown, according to an exemplary embodiment. In various embodiments, smart lobby system 1100 is configured to manage various processes associated with verifying, welcoming, and directing individuals entering a building as well as improvements thereto. For example, smart lobby system 1100 is configured to identify an individual entering smart lobby 1180, traverse a digital twin graph or other data structure and identify a digital twin graph associated with the individual to retrieve context information associated with the individual such as historical actions associated with the individual, determine based on the historical actions that the individual typically orders a coffee in the morning, and communicate with a hospitality system to automatically (e.g., without user intervention and/or with minimal user intervention, etc.) order a coffee to an office of the individual. As another example, smart lobby system 1100 is configured to receive sensor data associated with an individual (e.g., an image of the individual, etc.), identify the individual based on the sensor data (e.g., by traversing an entity graph to identify a digital twin having facial image data corresponding to the image data, etc.), retrieve context information associated with the identified individual (e.g., security data retrieved from a digital twin associated with the individual, etc.), and control access control devices based on the context information to grant the individual access to a building (e.g., in response to determining based on the security data that the individual is authorized to access the building, etc.). In various embodiments, smart lobby system 1100 is configured to communicate with external systems via network 20. For example, smart lobby system 1100 may communicate with client devices 40, building management system 102, cloud building management platform 140, and smart parking lot system 1155, hospitality system 1165, and smart lobby 1180. Hospitality system 1165 may include a system for food and beverage orders within building 10. For example, hospitality system 1165 may include an office services system of building 10 and/or a catering service of building 10. In some embodiments, a user may request food and drink at a meeting via hospitality system 1165. In various embodiments, smart parking lot system 1155 is configured to manage various processes associated with scheduling, assigning, and implementing parking in a parking lot as well as improvements thereto. For example, smart parking lot system 1155 is configured to identify an individual associated with a vehicle entering a smart parking lot, retrieve context information associated with the individual, dynamically assign the individual a parking space based on the context information, and direct the individual to the parking space. In some embodiments, client devices 40 may not be part of smart lobby system 1100, but may be user devices such as smartphones from which smart lobby system 1100 receives input data.


In various embodiments, smart lobby system 1100 implements and/or facilitates various features 1101 (e.g., as shown in FIG. 4B, etc.). For example, smart lobby system 1100 may implement arrival notification 1109 (e.g., notifying security of the arrival of a visitor, notifying a host of the arrival of a guest, notifying reception of the arrival of a visitor, etc.), food/beverage ordering 1107 (e.g., automatically ordering food/beverage for an employee when they arrive at work, etc.), visitor management 1121 (e.g., detecting visitors and directing visitors, for example to a meeting, etc.). In some embodiments, smart lobby system 1100 notifies a host 1111, security personnel 1113, and/or reception personnel 1115 in response to events. For example, smart lobby system 1100 may identify an individual arriving in smart lobby 1180 and notify a host 1111 that the individual has arrived. As an additional example, smart lobby system 1100 may receive an indication from smart parking lot system 1155 that a visitor has arrived and may notify reception personnel 1115 that the visitor has arrived. In some embodiments, smart lobby system 1100 facilitates food/beverage services 1107. For example, smart lobby system 1100 may identify an individual arriving in smart lobby 1180, determine a coffee order for the individual (e.g., by analyzing historical food/beverage orders of the individual, etc.), and communicate with hospitality system 1165 to have the coffee delivered to an office of the individual. In some embodiments, smart lobby system 1100 facilitates license plate recognition 1117 to identify visitor arrival 1119. For example, smart lobby system 1100 may integrate with smart parking lot system 1155 to capture an image of a vehicle, apply license plate recognition to identify a license plate number associated with the vehicle, and identify the vehicle as being associated with a visitor. Additionally or alternatively, smart lobby system 1100 may facilitate facial recognition 1123. For example, smart lobby system 1100 may capture image data of an arriving visitor, and apply facial recognition to identify the visitor.


In various embodiments, smart lobby system 1100 receives inputs 1103 from various sources. For example, smart lobby system 1100 may receive input data from a host 1139 (e.g., a reception host taking a visitors information, etc.), email 1137 (e.g., by extracting visitor information from a meeting invite, etc.), wayfinding data 1133 (e.g., directional data, map data, etc.), maps 1135, a digital twin 1129, context information 1131, QR 1125 (e.g., a visitor scanning an identifying QR code upon arrival, etc.), entity graph 170, and/or profile information 1127 (e.g., a virtual ticket associated with a visitor, a profile information included in a digital twin of an individual, etc.). Although several input 1103 sources have been recited, it should be understood that smart lobby system 1100 is not limited to the input sources explicitly recited herein and may dynamically and flexibly accommodate additional input sources (e.g., via an API, additional databases, etc.).


Smart lobby 1180 may be any space (e.g., lobby, entry, reception, etc.) configured to communicate with smart lobby system 1100. In various embodiments, building 10 includes smart lobby 1180. Smart lobby 1180 may include user interface 1182, access control devices 1184, and sensors 1186. User interface 1182 may include displays, voice assistants, gesture detectors, tablets, control panels, remotes, mobile applications, and/or any other user interface (e.g., physical or otherwise, etc.). In various embodiments, a user may interact with smart lobby system 1100 and/or smart lobby 1180 via user interface 1182. For example, a user may check-in at an automated reception desk of smart lobby 1180 using user interface 1182. Access control devices 1184 may include gates, turnstiles, ramps, doors, elevators, barriers, and/or any other devices or structures configured to control access to smart lobby 1180 and/or building 10. Sensors 1186, may include occupancy sensors, cameras (e.g., for facial recognition, etc.), biometric sensors, light sensors, weight sensors, temperature sensors, microphones (e.g., for voice recognition, etc.), and any other type of sensor. In various embodiments, sensors 1186 collect information about an individual in building 10 and/or smart lobby 1180 and transmit the information to smart lobby system 1100. For example, sensors 1186 may capture an image of an individual entering smart lobby 1180. To continue the example, smart lobby system 1100 may apply facial recognition to the image to identify facial features, traverse an entity graph data structure (e.g., entity graph 170, etc.) to identify a digital twin associated with the facial features (e.g., having a node corresponding to the facial features, including a connection “hasFace,” etc.), retrieve context information including a schedule of the individual from the identified digital twin, identify a next event on the schedule, and display directions on a digital display in smart lobby 1180 to direct the individual to a location of the next event. In some embodiments, sensors 1186 include a QR code reader. For example, an employee may scan an identification badge on a QR code reader when they enter smart lobby 1180.


Smart lobby system 1100 includes communications interface 1102, and processing circuit 1104. Communications interface 1102 is configured to facilitate communication between smart lobby system 1100 and external systems. For example, communications interface 1102 may facilitate communication between smart lobby system 1100 and smart lobby 1180. In some embodiments, communications interface 1102 is similar to communications interface 104 described above with reference to FIG. 1A.


Processing circuit 1104 includes processor 1106 and memory 1108. Smart lobby system 1100 may include one or more processing circuits 1104 including one or more processors 1106 and one or more memories 1108. Each of processors 1106 can be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. Each of processors 1106 is configured to execute computer code or instructions stored in memory 1108 or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.).


Memory 1108 may include one or more devices (e.g., memory units, memory devices, storage devices, or other computer-readable medium) for storing data and/or computer code for completing and/or facilitating the various processes described in the present disclosure. Memory 1108 may include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. Memory 1108 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. Memory 1108 may be communicably connected to processor(s) 1106 via processing circuit 1104 and may include computer code for executing (e.g., by processor 1106) one or more processes described herein.


Memory 1108 includes identification circuit 1110, context circuit 1120, notification circuit 1130, and frictionless access circuit 1140. Identification circuit 1110 may receive data from sensors 1186 and identify individuals. For example, identification circuit 1100 may receive an image of an individual and identify facial features of the individual from the image and identify a digital twin from a database corresponding to the individual based on the identified facial features. In some embodiments, identification circuit 1110 may use facial recognition to determine a facial features of an individual in an image, search an entity graph structure (e.g., entity graph 170, etc.) using the facial features to determine an individual having the facial features (e.g., a digital twin having a node including the facial features in a “hasFace” connection, etc.), and retrieve a digital twin of the individual (e.g., structured data associated with/representing the individual, etc.). In various embodiments, identification circuit 1110 queries structured data to facilitate identification. For example, identification circuit 1110 may query entity graph 170 with biometric data (e.g., facial image data, etc.) to identify an individual. As a further example, identification circuit 1110 may query a database of digital twins to identify an individual having a specific badge number (or other identifier). In some embodiments, identification circuit 1110 identifies individuals using biometric data. For example, identification circuit 1110 may use facial recognition and/or voice recognition to identify an individual. Additionally or alternatively, identification circuit 1110 may identify an individual using information received from a mobile device associated with the individual. For example, identification circuit 1110 may detect a mobile phone using NFC, receive an identifier from the mobile phone, and search a graph structure (e.g., entity graph 170, etc.) to determine an individual associated with the mobile phone.


Context circuit 1120 is configured to analyze context information associated with an identified individual. For example, context analysis circuit 1120 may analyze a schedule of an individual, determine that the individual has a meeting that they are late for, and facilitate displaying a route to the meeting on digital displays located throughout smart lobby 1180. As a further example, context analysis circuit 1120 may analyze a digital twin associated with an individual, determine that the individual is mobility restricted, and identify security personnel to assist the individual. Additionally or alternatively, context circuit 1120 may analyze context information associated with smart lobby 1180. For example, context circuit 1120 may monitor an occupancy metric (e.g., a number of individuals in smart lobby 1180, etc.). In various embodiments, context circuit 1120 may perform actions in response to detecting an individual arrive in smart lobby 1180. For example, context circuit 1120 may analyze historical actions of the individual such as an food/beverage ordering history of the individual and generate a food/beverage order for the individual in response to detecting the individual enter smart lobby 1180 (e.g., when the user arrives at building 10 in the morning, etc.). In some embodiments, entity graph 170 includes context information. In various embodiments, context circuit 1120 facilitates actions based on an occupancy of smart lobby 1180. For example, context circuit 1120 may determine that there is a large number of individuals in smart lobby 1180 and may notify a number of the individuals to redirect them to alternate entrances. As a further example, context circuit 1120 may request additional reception personnel if smart lobby 1180 becomes backed up. As yet another example, context circuit 1120 may generate a cookie order for hospitality system 1165 in response to determining that visitors have been waiting in line for a long period of time in smart lobby 1180. In various embodiments, the occupancy metric is a number of individuals in smart lobby 1180. The number of individuals may be determined dynamically (e.g., changes over time, based on environmental conditions, etc.) and/or may be absolute (e.g., a static value, etc.).


Notification circuit 1130 is configured to generate and transmit notifications. For example, notification circuit 1130 may receive an indication that an individual has arrived in building 10 and/or smart lobby 1180 and receive an indication of an interested person (e.g., from context circuit 1120, etc.) and generate a notification to the interested person indicating that the individual has arrived in building 10 and/or smart lobby 1180. In some embodiments, notification circuit 1130 may receive sensor data from sensors 1186 indicating that an individual has been detected in smart lobby 1180, receive context information including an identity of an interested person, and transmit a notification to the interested person indicating a location of the individual. Additionally or alternatively, notification circuit 1130 may control devices associate with user interface 1182. For example, user interface 1182 may include digital signage located throughout smart lobby 1180 and notification circuit 1130 may be configured to transmit user interface elements to the digital signage for displaying to viewers. For example, notification circuit 1130 may control a digital display associated with user interface 1182 to display turn-by-turn directions to next event on a schedule of an identified individual. In various embodiments, notification circuit 1130 facilitates two-way communication. For example, notification circuit 1130 may send a notification to an employee indicating that a guest of the employee has arrived in smart lobby 1180, may receive a response from the employee (e.g., a personalized message, etc.), and may display the response to the guest (e.g., via user interface 1182, etc.).


Frictionless access circuit 1140 is configured to facilitate frictionless access to building 10 and/or smart lobby 1180. For example, frictionless access circuit 1140 may control access control devices 1184 to automatically (e.g., with little to no user intervention, etc.) grant users access to building 10 and/or smart lobby 1180. In various embodiments, frictionless access circuit 1140 retrieves security information associated with an individual. For example, frictionless access circuit 1140 may traverse an entity graph data structure (e.g., entity graph 170, etc.) to identify a digital twin representing an individual and including security information associated with the individual (e.g., access rights, alerts, clearances, warnings, etc.). In various embodiments, frictionless access circuit 1140 analyzes the security information to determine actions. For example, frictionless access circuit 1140 may analyze the security information to determine that an individual has access to building 10 and control access control devices 1184 to grant the individual access to the building 10. As a further example, frictionless access circuit 1140 may analyze the security information to determine that an individual is banned from building 10 and notify security personnel (e.g., via notification circuit 1130, etc.) of the presence of the individual. In some embodiments, frictionless access circuit 1140 receives security information from sensors 1186. For example, sensors 1186 may receive a virtual ticket having security information from a mobile device associated with an individual (e.g., via NFC, etc.), and transmit the virtual ticket to frictionless access circuit 1140. Additionally or alternatively, sensors 1186 may detect a mobile device using Bluetooth, WiFi, and/or a cellular connection (e.g., via a 4G or 5G access point/small cell base station, etc.). In various embodiments, frictionless access circuit 1140 controls a user's access to building 10. For example, frictionless access circuit 1140 may receive a location of a meeting associated with a visitor and may control access control devices 1184 to grant the visitor access to a route to a conference room associated with the meeting without granting the visitor access to sections of building 10 not related to the meeting and/or a route to the meeting. Additionally or alternatively, frictionless access circuit 1140 may interact with users via user interface 1182. For example, frictionless access circuit 1140 may facilitate two-factor authentication using user interface 1182. As a further example, a user may supply a numeric code using user interface 1182 and frictionless access circuit 1140 may verify the user using the numeric code.


Referring now to FIG. 5, a method 1200 of performing actions in response to the arrival of an individual is shown, according to an exemplary embodiment. In various embodiments, smart lobby system 1100 performs method 1200. For example, smart lobby system 1100 may perform method 1200 in response to detecting an individual in building 10 for the first time in a day (e.g., when the individual arrives at work in the morning, etc.). At step 1210, smart lobby system 1100 detects an individual who enters into a lobby. For example, sensors 1186 may detect motion in smart lobby 1180 and capture an image of an individual, and identification circuit 1110 may identify the individual by using facial recognition on the image and searching through an entity graph data structure (e.g., entity graph 170, etc.) to identify a digital twin associated with the individual (e.g., a digital twin representing the individual, etc.). As an additional example, sensors 1186 may receive a communication including an identifier from a mobile device associated with a visitor and identify a digital twin representing the individual using the identifier. In various embodiments, detecting the individual includes identifying the individual. For example, smart lobby system 1100 may identify an individual based on behavior patterns of the individual (e.g., a path taken by the individual, gait analysis performed on the individual, traversing entity graph 170, etc.).


At step 1220, smart lobby system 1100 retrieves context information corresponding to the individual. For example, context circuit 1120 may traverse a digital twin graph representing the individual to retrieve a schedule of the individual and security information associated with the individual. As a further example, context circuit 1120 may retrieve historical actions associated with the individual from a digital twin associated with the individual. In some embodiments, context circuit 1120 may retrieve timeseries actions of the individual based on an “isLinked” relationship in a digital twin of the individual. For example, the timeseries actions may describe a food/beverage order history of the individual. In various embodiments, the context information includes characteristics of the individual, identifying information associated with the individual, historical information associated with the individual, and any other information associated with an individual. Additionally or alternatively, context information may be associated with a space. For example, context information may describe characteristics of a space, environmental parameters of a space, historical information associated with a space, user population information associated with a space, and the like.


At step 1230, smart lobby system 1100 updates a user profile of the individual to indicate that the individual has arrived at the lobby. For example, smart lobby system 1100 may update a digital twin representing the individual to include a timeseries data entry associated with the individual arriving at building 10. In various embodiments, a digital twin associated with the individual includes the user profile. As a further example, smart lobby system 1100 may update a database of historical actions associated with a digital twin of the individual to include a “check-in” time for the individual. In various embodiments, the user profile includes a digital twin of the individual. For example, smart lobby system 1100 may retrieve an entity graph data structure representing the individual and add a node including a location of smart lobby 1180 where the individual was detected and a time at which the individual was detected to the entity graph data structure. In some embodiments, updating the user profile triggers additional actions. For example, if the individual is late to work, updating the user profile indicating that the user is late may trigger an email notification to the individual indicating that the individual is late to work. In various embodiments, updating the user profile includes performing one or more actions. For example, updating the user profile may include generating a food/beverage order for hospitality system 1165 for the individual.


At step 1240, smart lobby system 1100 controls one or more access control devices based on the context information. For example, smart lobby system 1100 may determine that an individual is authorized to access building 10 (e.g., by analyzing security information included in the context information, etc.) and may control access control devices 1184 to grant the individual access to building 10 (e.g., by unlocking a door, etc.). In some embodiments, step 1240 includes controlling the individual's access to building 10. For example, a visitor may have a meeting in a conference room separated from smart lobby 1210 by two access control devices 1184. To continue the example, smart lobby system 1100 may control a first access control device 1184 to grant the individual access to a hallway. The hallway may include the second access control device 1184 and a side door. Smart lobby system 1100 may not open the side door (e.g., such that the visitor may not leave a designated route between smart lobby 1180 and the conference room, etc.) but may control the second access control device 1184 (e.g., in response to the access control device 1184 detecting the visitor nearby, etc.) to grant the visitor access to the conference room.


Referring now to FIG. 6, a method 1300 of generating an action based on an environmental characteristic of a lobby is shown, according to an exemplary embodiment. In various embodiments, smart lobby system 1100 performs method 1300. For example, smart lobby system 1100 may perform method 1300 in response to detecting an individual in building 10. At step 1310, smart lobby system 1100 detects an individual who enters into a lobby. For example, sensors 1186 may detect motion in smart lobby 1180 and capture an image of an individual, and identification circuit 1110 may identify the individual by using facial recognition on the image and searching through an entity graph data structure (e.g., entity graph 170, etc.) to identify a digital twin associated with the individual (e.g., a digital twin representing the individual, etc.). As an additional example, sensors 1186 may receive a communication including an identifier from a mobile device associated with a visitor and identify a digital twin representing the individual using the identifier.


At step 1320, smart lobby system 1100 retrieves context information corresponding to the individual. For example, context circuit 1120 may traverse a digital twin graph representing the individual to retrieve a schedule of the individual and security information associated with the individual. As a further example, context circuit 1120 may retrieve historical actions associated with the individual from a digital twin associated with the individual. In some embodiments, context circuit 1120 may retrieve timeseries actions of the individual based on an “isLinked” relationship in a digital twin of the individual. For example, the timeseries actions may describe a food/beverage order history of the individual.


At step 1330, smart lobby system 1100 determines environmental characteristics associated with the lobby. For example, smart lobby system 1100 may receive data from sensors 1186 and determine an occupancy of smart lobby 1180. As a further example, smart lobby system 1100 may determine a temperature of smart lobby 1180. In some embodiments, smart lobby system 1100 may receive an image from sensors 1186 (e.g., a security camera, etc.) and perform image recognition to determine an occupancy metric (e.g., a number of people waiting in line in smart lobby 1180, etc.). As a further example, smart lobby system 1100 may retrieve a reception calendar associated with smart lobby 1180 and determine an expected reception demand (e.g., based on the number of individuals scheduled to arrive over time, etc.).


At step 1340, smart lobby system 1100 generates an action based on the context information and the environmental characteristics of the lobby. For example, in response to determining that long wait times exist in smart lobby 1180, smart lobby system 1100 may transmit notifications to individuals in line in smart lobby 1180 to redirect them to alternative entrances (e.g., another lobby, etc.). As a further example, smart lobby system 1100 may determine that a large group of visitors are arriving at smart lobby 1180 (e.g., based on a reception schedule of smart lobby 1180, etc.), and may generate a notification to employees who are arriving in a parking lot associated with the building (e.g., via smart parking lot system 1155, etc.) to use an alternate entrance so that smart lobby 1180 does not become crowded. As a further example, smart lobby system 1100 may determine that smart lobby 1180 includes a large number of individuals and may generate a notification to request additional security personnel to facilitate piggybacking reduction (e.g., monitor smart lobby 1180 and ensure that individuals aren't piggybacking into building 10 with one another, etc.). In some embodiments, smart lobby system 1100 may determine that smart lobby 1180 is overcrowded, analyze context information associated with individuals in smart lobby 1180 to determine if they are authorized to access building 10, and in response to determining that they are authorized, display a message on digital signage (e.g., via user interface 1182, etc.) that the individual has been granted access to building 10, and control access control devices 1184 to grant the individual access to building 10.


Referring now to FIG. 7, an example embodiment of smart lobby 1180 is shown. At step 1410, smart lobby system 1100 may identify an individual who enters into smart lobby 1180. For example, sensor 1182 may capture an image of the individual and smart lobby system 1100 may use facial recognition to identify a digital twin of the individual as described in detail above. As an additional example, sensors 1182 may capture behavior patterns (e.g., path taken, sequence of actions performed, interactions, etc.) of the individual and query entity graph 170 to determine an identity of the individual. At step 1420, smart lobby system 1100 retrieves context information corresponding to the individual. In some embodiments, smart lobby system 1100 may retrieve a virtual ticket associated with the individual indicating that the individual is a visitor and including a schedule of the individual and host of the individual. At step 1430, smart lobby system 1100 may update a smart display to greet the individual. For example, smart lobby system 1100 may display a personalized greeting to the individual, a schedule of the individual, and instructions to wait for an employee escort. At step 1440, smart lobby system 1100 may transmit a notification to an employee. For example, smart lobby system 1100 may transmit a notification to the host of the visitor indicating that the visitor has arrived in smart lobby 1180 and that they should greet the visitor and escort the visitor to a meeting. At step 1450, smart lobby system 1100 controls an access control device 1188 to grant the individual access to the building. For example, smart lobby system 1100 may unlock a turnstile in response to an employee escort joining the visitor.


Configuration of Exemplary Embodiments


The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements can be reversed or otherwise varied and the nature or number of discrete elements or positions can be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps can be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure.


The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure can be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.


Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also two or more steps can be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.


The term “client or “server” include all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing. The apparatus may include special purpose logic circuitry, e.g., a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC). The apparatus may also include, in addition to hardware, code that creates an execution environment for the computer program in question (e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them). The apparatus and execution environment may realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.


The systems and methods of the present disclosure may be completed by any computer program. A computer program (also known as a program, software, software application, script, or code) may be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program may be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.


The processes and logic flows described in this specification may be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows may also be performed by, and apparatus may also be implemented as, special purpose logic circuitry (e.g., an FPGA or an ASIC).


Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data (e.g., magnetic, magneto-optical disks, or optical disks). However, a computer need not have such devices. Moreover, a computer may be embedded in another device (e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), etc.). Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD ROM and DVD-ROM disks). The processor and the memory may be supplemented by, or incorporated in, special purpose logic circuitry. The term processing circuit as used herein may include hardware, software, or any combination thereof. For example, a processing circuit may include a processor and memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to perform operations.


To provide for interaction with a user, implementations of the subject matter described in this specification may be implemented on a computer having a display device (e.g., a CRT (cathode ray tube), LCD (liquid crystal display), OLED (organic light emitting diode), TFT (thin-film transistor), or other flexible configuration, or any other monitor for displaying information to the user and a keyboard, a pointing device, e.g., a mouse, trackball, etc., or a touch screen, touch pad, etc.) by which the user may provide input to the computer. Other kinds of devices may be used to provide for interaction with a user as well; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic, speech, or tactile input. In addition, a computer may interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.


Implementations of the subject matter described in this disclosure may be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer) having a graphical user interface or a web browser through which a user may interact with an implementation of the subject matter described in this disclosure, or any combination of one or more such back end, middleware, or front end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a LAN and a WAN, an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).


The present disclosure may be embodied in various different forms, and should not be construed as being limited to only the illustrated embodiments herein. Rather, these embodiments are provided as examples so that this disclosure will be thorough and complete, and will fully convey the aspects and features of the present disclosure to those skilled in the art. Accordingly, processes, elements, and techniques that are not necessary to those having ordinary skill in the art for a complete understanding of the aspects and features of the present disclosure may not be described. Unless otherwise noted, like reference numerals denote like elements throughout the attached drawings and the written description, and thus, descriptions thereof may not be repeated. Further, features or aspects within each example embodiment should typically be considered as available for other similar features or aspects in other example embodiments.


It will be understood that, although the terms “first,” “second,” “third,” etc., may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms are used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section described below could be termed a second element, component, region, layer or section, without departing from the spirit and scope of the present disclosure.


The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting of the present disclosure. As used herein, the singular forms “a” and “an” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes,” and “including,” “has,” “have,” and “having,” when used in this specification, specify the presence of the 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, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Expressions such as “at least one of,” when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list.


As used herein, the term “substantially,” “about,” and similar terms are used as terms of approximation and not as terms of degree, and are intended to account for the inherent variations in measured or calculated values that would be recognized by those of ordinary skill in the art. Further, the use of “may” when describing embodiments of the present disclosure refers to “one or more embodiments of the present disclosure.” As used herein, the terms “use,” “using,” and “used” may be considered synonymous with the terms “utilize,” “utilizing,” and “utilized,” respectively. Also, the term “exemplary” is intended to refer to an example or illustration.


A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

Claims
  • 1. One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to: detect an individual who enters into a lobby of a building;retrieve context information corresponding to the individual;update a portion of a digital twin of the individual to indicate the individual has arrived at the building, the digital twin including the context information representing an access right of the individual; andcontrol one or more access control devices to grant the individual access to the building based on the context information.
  • 2. The one or more non-transitory computer-readable storage media of claim 1, wherein detecting the individual includes recognizing a physical characteristic of the individual.
  • 3. The one or more non-transitory computer-readable storage media of claim 1, wherein the context information includes a security metric associated with the individual.
  • 4. The one or more non-transitory computer-readable storage media of claim 3, wherein controlling the one or more access control devices is in response to comparing the security metric to a threshold.
  • 5. The one or more non-transitory computer-readable storage media of claim 1, wherein the context information includes a schedule having events associated with the individual.
  • 6. The one or more non-transitory computer-readable storage media of claim 5, wherein controlling the one or more access control devices further includes displaying directions to the individual to a location of a next event on the schedule of the individual.
  • 7. The one or more non-transitory computer-readable storage media of claim 1, wherein the context information includes an interested person.
  • 8. The one or more non-transitory computer-readable storage media of claim 7, wherein updating the digital twin of the individual includes transmitting a notification to a device associated with the interested person indicating that the individual has arrived in the lobby.
  • 9. The one or more non-transitory computer-readable storage media of claim 1, wherein the context information includes user preferences of the individual.
  • 10. The one or more non-transitory computer-readable storage media of claim 9, wherein updating the digital twin of the individual includes transmitting a notification to a provider of at least one of food or beverage based on the user preferences of the individual.
  • 11. One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to: detect a vehicle that enters into a parking lot;identify an individual associated with the vehicle;retrieve context information corresponding to the individual;update a portion of a digital twin of the individual to indicate the individual has arrived in the parking lot, the digital twin including the context information representing an access right of the individual;transmit a notification to a lobby personnel indicating that the individual has arrived in the parking lot; andcontrol one or more access control devices to grant the individual access to a building associated with the parking lot based on the context information.
  • 12. The one or more non-transitory computer-readable storage media of claim 11, wherein detecting the vehicle includes recognizing a license plate of the vehicle.
  • 13. The one or more non-transitory computer-readable storage media of claim 11, wherein the context information includes a security metric associated with the individual.
  • 14. The one or more non-transitory computer-readable storage media of claim 13, wherein controlling the one or more access control devices is in response to comparing the security metric to a threshold.
  • 15. The one or more non-transitory computer-readable storage media of claim 11, wherein identifying the individual includes recognizing a physical characteristic of the individual.
  • 16. The one or more non-transitory computer-readable storage media of claim 11, wherein the context information includes a schedule having events associated with the individual.
  • 17. The one or more non-transitory computer-readable storage media of claim 16, wherein controlling the one or more access control devices further includes displaying to the individual directions to a location of a next event on the schedule of the individual.
  • 18. The one or more non-transitory computer-readable storage media of claim 11, wherein the context information includes interested persons.
  • 19. The one or more non-transitory computer-readable storage media of claim 18, wherein the one or more processors transmit a notification to a device associated with at least one of the interested persons indicating that the individual has arrived in the parking lot.
  • 20. One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to: detect an individual who enters into a lobby of a building;retrieve context information corresponding to the individual;update at least a portion of a digital twin to indicate the individual has arrived at the building, the digital twin including the context information representing an access right of the individual;determine environmental characteristics of the lobby; andgenerate an action based on the context information and the environmental characteristics of the lobby.
CROSS-REFERENCE TO RELATED APPLICATION

The present Application claims the benefit and priority to U.S. Provisional Patent Application No. 62/794,370, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,276, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,533, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,535, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,389, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,393, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,415, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,032, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,357, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,348, filed on Jan. 18, 2019, 62/794,407, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,502, filed on Jan. 18, 2019, U.S. Provisional Patent Application No. 62/794,489, filed on Jan. 18, 2019, the entire disclosures of each of which are incorporated by reference herein.

US Referenced Citations (477)
Number Name Date Kind
5301109 Landauer et al. Apr 1994 A
5446677 Jensen et al. Aug 1995 A
5581478 Cruse et al. Dec 1996 A
5812962 Kovac Sep 1998 A
5960381 Singers et al. Sep 1999 A
5973662 Singers et al. Oct 1999 A
6014612 Larson et al. Jan 2000 A
6031547 Kennedy Feb 2000 A
6134511 Subbarao Oct 2000 A
6157943 Meyer Dec 2000 A
6285966 Brown et al. Sep 2001 B1
6363422 Hunter et al. Mar 2002 B1
6385510 Hoog et al. May 2002 B1
6389331 Jensen et al. May 2002 B1
6401027 Xu et al. Jun 2002 B1
6437691 Sandelman et al. Aug 2002 B1
6477518 Li et al. Nov 2002 B1
6487457 Hull et al. Nov 2002 B1
6493755 Hansen et al. Dec 2002 B1
6577323 Jamieson et al. Jun 2003 B1
6626366 Kayahara et al. Sep 2003 B2
6646660 Patty Nov 2003 B1
6704016 Oliver et al. Mar 2004 B1
6732540 Sugihara et al. May 2004 B2
6764019 Kayahara et al. Jul 2004 B1
6782385 Natsumeda et al. Aug 2004 B2
6813532 Eryurek et al. Nov 2004 B2
6816811 Seem Nov 2004 B2
6823680 Jayanth Nov 2004 B2
6826454 Sulfstede Nov 2004 B2
6865511 Frerichs et al. Mar 2005 B2
6925338 Eryurek et al. Aug 2005 B2
6986138 Sakaguchi et al. Jan 2006 B1
7031880 Seem et al. Apr 2006 B1
7401057 Eder Jul 2008 B2
7552467 Lindsay Jun 2009 B2
7627544 Chkodrov et al. Dec 2009 B2
7818249 Lovejoy et al. Oct 2010 B2
7889051 Billig et al. Feb 2011 B1
7996488 Casabella et al. Aug 2011 B1
8078330 Brickfield et al. Dec 2011 B2
8104044 Scofield et al. Jan 2012 B1
8180663 Tischhauser et al. May 2012 B2
8229470 Ranjan et al. Jul 2012 B1
8346589 Norton et al. Jan 2013 B1
8401991 Wu et al. Mar 2013 B2
8495745 Schrecker et al. Jul 2013 B1
8516016 Park et al. Aug 2013 B2
8532808 Drees et al. Sep 2013 B2
8532839 Drees et al. Sep 2013 B2
8600556 Nesler et al. Dec 2013 B2
8629755 Hashim-Waris Jan 2014 B2
8635182 MacKay Jan 2014 B2
8682921 Park et al. Mar 2014 B2
8731724 Drees et al. May 2014 B2
8737334 Ahn et al. May 2014 B2
8738334 Jiang et al. May 2014 B2
8751487 Byrne et al. Jun 2014 B2
8788097 Drees et al. Jul 2014 B2
8805995 Oliver Aug 2014 B1
8843238 Wenzel et al. Sep 2014 B2
8874071 Sherman et al. Oct 2014 B2
8907803 Martin Dec 2014 B2
8941465 Pineau et al. Jan 2015 B2
8990127 Taylor Mar 2015 B2
9070113 Shafiee et al. Jun 2015 B2
9116978 Park et al. Aug 2015 B2
9185095 Moritz et al. Nov 2015 B1
9189527 Park et al. Nov 2015 B2
9196009 Drees et al. Nov 2015 B2
9202322 Kappeler et al. Dec 2015 B2
9229966 Aymeloglu et al. Jan 2016 B2
9286582 Drees et al. Mar 2016 B2
9311807 Schultz et al. Apr 2016 B2
9344751 Ream et al. May 2016 B1
9354968 Wenzel et al. May 2016 B2
9507686 Horn et al. Nov 2016 B2
9524594 Ouyang et al. Dec 2016 B2
9558196 Johnston et al. Jan 2017 B2
9576255 Kalb et al. Feb 2017 B2
9652813 Gifford et al. May 2017 B2
9666075 Davies et al. May 2017 B2
9753455 Drees Sep 2017 B2
9811249 Chen et al. Nov 2017 B2
9838844 Emeis et al. Dec 2017 B2
9886478 Mukherjee Feb 2018 B2
9948359 Horton Apr 2018 B2
9955318 Scheper et al. Apr 2018 B1
10055114 Shah et al. Aug 2018 B2
10055206 Park et al. Aug 2018 B2
10116461 Fairweather et al. Oct 2018 B2
10154138 Te Booij et al. Dec 2018 B2
10169454 Ait-Mokhtar et al. Jan 2019 B2
10171297 Stewart et al. Jan 2019 B2
10171586 Shaashua et al. Jan 2019 B2
10187258 Nagesh et al. Jan 2019 B2
10354531 Bronder et al. Jul 2019 B1
10380854 Yu Aug 2019 B1
10505756 Park et al. Dec 2019 B2
10514963 Shrivastava et al. Dec 2019 B2
10515098 Park et al. Dec 2019 B2
10534326 Sridharan et al. Jan 2020 B2
10536295 Fairweather et al. Jan 2020 B2
10564993 Deutsch et al. Feb 2020 B2
10705492 Harvey Jul 2020 B2
10708078 Harvey Jul 2020 B2
10760815 Janakiraman et al. Sep 2020 B2
10762475 Song et al. Sep 2020 B2
10824120 Ahmed Nov 2020 B2
10845771 Harvey Nov 2020 B2
10854194 Park et al. Dec 2020 B2
10862928 Badawy et al. Dec 2020 B1
10921760 Harvey Feb 2021 B2
10921972 Park et al. Feb 2021 B2
10969133 Harvey Apr 2021 B2
10986121 Stockdale et al. Apr 2021 B2
11016998 Park et al. May 2021 B2
11024292 Park et al. Jun 2021 B2
11038709 Park et al. Jun 2021 B2
11041650 Li et al. Jun 2021 B2
11054796 Holaso Jul 2021 B2
11070390 Park et al. Jul 2021 B2
11073976 Park et al. Jul 2021 B2
11108587 Park et al. Aug 2021 B2
11113295 Park et al. Sep 2021 B2
11127235 Sinha et al. Sep 2021 B2
11229138 Harvey et al. Jan 2022 B1
11314726 Park et al. Apr 2022 B2
11314788 Park et al. Apr 2022 B2
20020010562 Schleiss et al. Jan 2002 A1
20020016639 Smith et al. Feb 2002 A1
20020059229 Natsumeda et al. May 2002 A1
20020123864 Eryurek et al. Sep 2002 A1
20020147506 Eryurek et al. Oct 2002 A1
20020169514 Eryurek et al. Nov 2002 A1
20020177909 Fu et al. Nov 2002 A1
20020188557 Ochiai Dec 2002 A1
20030005486 Ridolfo et al. Jan 2003 A1
20030014130 Grumelart Jan 2003 A1
20030073432 Meade, II Apr 2003 A1
20030150908 Pokorny et al. Aug 2003 A1
20030158704 Triginai et al. Aug 2003 A1
20030158795 Markham et al. Aug 2003 A1
20030171851 Brickfield et al. Sep 2003 A1
20030200059 Ignatowski et al. Oct 2003 A1
20040068390 Saunders Apr 2004 A1
20040128314 Katibah et al. Jul 2004 A1
20040133314 Ehlers et al. Jul 2004 A1
20040199360 Friman et al. Oct 2004 A1
20040252288 Kacyra et al. Dec 2004 A1
20050055308 Meyer et al. Mar 2005 A1
20050108262 Fawcett et al. May 2005 A1
20050154494 Ahmed Jul 2005 A1
20050278703 Lo et al. Dec 2005 A1
20050283337 Sayal Dec 2005 A1
20060015376 Sattler et al. Jan 2006 A1
20060095521 Patinkin May 2006 A1
20060140207 Eschbach et al. Jun 2006 A1
20060184479 Levine Aug 2006 A1
20060200476 Gottumukkala et al. Sep 2006 A1
20060227010 Berstis et al. Oct 2006 A1
20060265751 Cosquer et al. Nov 2006 A1
20060271589 Horowitz et al. Nov 2006 A1
20070028179 Levin et al. Feb 2007 A1
20070203693 Estes Aug 2007 A1
20070250417 Lane et al. Oct 2007 A1
20070250920 Lindsay Oct 2007 A1
20070261062 Bansal et al. Nov 2007 A1
20070273497 Kuroda et al. Nov 2007 A1
20070273610 Baillot Nov 2007 A1
20080034425 Overcash et al. Feb 2008 A1
20080094230 Mock et al. Apr 2008 A1
20080097816 Freire et al. Apr 2008 A1
20080162198 Jabbour et al. Jul 2008 A1
20080186160 Kim et al. Aug 2008 A1
20080249756 Chaisuparasmikul Oct 2008 A1
20080252723 Park Oct 2008 A1
20080263467 Wilkins Oct 2008 A1
20080266383 Shah et al. Oct 2008 A1
20080281472 Podgorny et al. Nov 2008 A1
20090070407 Castle et al. Mar 2009 A1
20090195349 Frader-Thompson et al. Aug 2009 A1
20100045439 Tak et al. Feb 2010 A1
20100058248 Park Mar 2010 A1
20100128931 Bongard May 2010 A1
20100131533 Ortiz May 2010 A1
20100274366 Fata et al. Oct 2010 A1
20100281387 Holland et al. Nov 2010 A1
20100286937 Hedley et al. Nov 2010 A1
20100324962 Nesler et al. Dec 2010 A1
20110015802 Imes Jan 2011 A1
20110047418 Drees et al. Feb 2011 A1
20110054968 Galaviz Mar 2011 A1
20110061015 Drees et al. Mar 2011 A1
20110071685 Huneycutt et al. Mar 2011 A1
20110077950 Hughston Mar 2011 A1
20110087650 MacKay et al. Apr 2011 A1
20110087988 Ray et al. Apr 2011 A1
20110088000 MacKay Apr 2011 A1
20110125737 Pothering et al. May 2011 A1
20110137853 MacKay Jun 2011 A1
20110153603 Adiba et al. Jun 2011 A1
20110154363 Karmarkar Jun 2011 A1
20110157357 Weisensale et al. Jun 2011 A1
20110178977 Drees Jul 2011 A1
20110184943 Norton et al. Jul 2011 A1
20110191343 Heaton et al. Aug 2011 A1
20110205022 Cavallaro et al. Aug 2011 A1
20110218777 Chen et al. Sep 2011 A1
20120011126 Park et al. Jan 2012 A1
20120011141 Park et al. Jan 2012 A1
20120022698 MacKay Jan 2012 A1
20120062577 Nixon Mar 2012 A1
20120064923 Imes et al. Mar 2012 A1
20120083930 Ilic et al. Apr 2012 A1
20120100825 Sherman et al. Apr 2012 A1
20120101637 Imes et al. Apr 2012 A1
20120112929 Gupta et al. May 2012 A1
20120135759 Imes et al. May 2012 A1
20120136485 Weber et al. May 2012 A1
20120143356 Berg-Sonne et al. Jun 2012 A1
20120158633 Eder Jun 2012 A1
20120259583 Noboa et al. Oct 2012 A1
20120272228 Marndi et al. Oct 2012 A1
20120278051 Jiang et al. Nov 2012 A1
20120310852 Ramalingamoorthy et al. Dec 2012 A1
20120323643 Volz Dec 2012 A1
20120326893 Glezerman Dec 2012 A1
20130007063 Kalra et al. Jan 2013 A1
20130038430 Blower et al. Feb 2013 A1
20130038707 Cunningham et al. Feb 2013 A1
20130060820 Bulusu et al. Mar 2013 A1
20130086497 Ambuhl et al. Apr 2013 A1
20130097706 Titonis et al. Apr 2013 A1
20130103221 Raman et al. Apr 2013 A1
20130167035 Imes et al. Jun 2013 A1
20130170710 Kuoch et al. Jul 2013 A1
20130173062 Koenig-Richardson Jul 2013 A1
20130204836 Choi et al. Aug 2013 A1
20130226320 Berg-Sonne et al. Aug 2013 A1
20130246916 Reimann et al. Sep 2013 A1
20130247205 Schrecker et al. Sep 2013 A1
20130262035 Mills Oct 2013 A1
20130275174 Bennett et al. Oct 2013 A1
20130275908 Reichard Oct 2013 A1
20130297050 Reichard et al. Nov 2013 A1
20130298244 Kumar et al. Nov 2013 A1
20130331995 Rosen Dec 2013 A1
20130338970 Reghetti Dec 2013 A1
20140032506 Hoey et al. Jan 2014 A1
20140059483 Mairs et al. Feb 2014 A1
20140081652 Klindworth Mar 2014 A1
20140135952 Maehara May 2014 A1
20140139359 Paul et al. May 2014 A1
20140152651 Chen et al. Jun 2014 A1
20140172184 Schmidt et al. Jun 2014 A1
20140189861 Gupta et al. Jul 2014 A1
20140207282 Angle et al. Jul 2014 A1
20140258052 Khuti et al. Sep 2014 A1
20140269614 Maguire et al. Sep 2014 A1
20140277765 Karimi et al. Sep 2014 A1
20140278461 Artz Sep 2014 A1
20140327555 Sager et al. Nov 2014 A1
20140334684 Strimling Nov 2014 A1
20140358285 Aggarwal et al. Dec 2014 A1
20150019174 Kiff et al. Jan 2015 A1
20150042240 Aggarwal et al. Feb 2015 A1
20150066716 Shortridge Mar 2015 A1
20150105917 Sasaki et al. Apr 2015 A1
20150138001 Davies May 2015 A1
20150145468 Ma et al. May 2015 A1
20150156030 Fadell et al. Jun 2015 A1
20150156031 Fadell et al. Jun 2015 A1
20150168931 Jin Jun 2015 A1
20150172300 Cochenour Jun 2015 A1
20150178421 Borrelli et al. Jun 2015 A1
20150185261 Frader-Thompson et al. Jul 2015 A1
20150186777 Lecue et al. Jul 2015 A1
20150202962 Habashima et al. Jul 2015 A1
20150204563 Imes et al. Jul 2015 A1
20150220942 Dubberley Aug 2015 A1
20150235267 Steube et al. Aug 2015 A1
20150241895 Lu et al. Aug 2015 A1
20150244730 Vu et al. Aug 2015 A1
20150244732 Golshan et al. Aug 2015 A1
20150261863 Dey et al. Sep 2015 A1
20150263900 Polyakov et al. Sep 2015 A1
20150286969 Warner et al. Oct 2015 A1
20150295796 Hsiao et al. Oct 2015 A1
20150304193 Ishii et al. Oct 2015 A1
20150310682 Arora et al. Oct 2015 A1
20150316918 Schleiss et al. Nov 2015 A1
20150324422 Elder Nov 2015 A1
20150341212 Hsiao et al. Nov 2015 A1
20150348417 Ignaczak et al. Dec 2015 A1
20150379080 Jochimski Dec 2015 A1
20160011753 McFarland et al. Jan 2016 A1
20160033946 Zhu et al. Feb 2016 A1
20160035246 Curtis Feb 2016 A1
20160049030 G Feb 2016 A1
20160065601 Gong et al. Mar 2016 A1
20160070736 Swan et al. Mar 2016 A1
20160078229 Gong et al. Mar 2016 A1
20160090839 Stolarczyk Mar 2016 A1
20160119434 Dong et al. Apr 2016 A1
20160127712 Alfredsson et al. May 2016 A1
20160134432 Hund et al. May 2016 A1
20160139752 Shim et al. May 2016 A1
20160163186 Davidson et al. Jun 2016 A1
20160170390 Xie et al. Jun 2016 A1
20160171785 Banatwala et al. Jun 2016 A1
20160171862 Das et al. Jun 2016 A1
20160173816 Huenerfauth et al. Jun 2016 A1
20160179315 Sarao et al. Jun 2016 A1
20160179342 Sarao et al. Jun 2016 A1
20160179990 Sarao et al. Jun 2016 A1
20160195856 Spero Jul 2016 A1
20160212165 Singla et al. Jul 2016 A1
20160239660 Azvine et al. Aug 2016 A1
20160239756 Aggour et al. Aug 2016 A1
20160247129 Song et al. Aug 2016 A1
20160260063 Harris et al. Sep 2016 A1
20160313751 Risbeck et al. Oct 2016 A1
20160313752 Przybylski Oct 2016 A1
20160313902 Hill et al. Oct 2016 A1
20160350364 Anicic et al. Dec 2016 A1
20160357521 Zhang et al. Dec 2016 A1
20160357828 Tobin et al. Dec 2016 A1
20160358432 Branscomb et al. Dec 2016 A1
20160363336 Roth et al. Dec 2016 A1
20160370258 Perez Dec 2016 A1
20160378306 Kresl et al. Dec 2016 A1
20160379326 Chan-Gove et al. Dec 2016 A1
20170006135 Siebel Jan 2017 A1
20170011318 Vigano et al. Jan 2017 A1
20170017221 Lamparter et al. Jan 2017 A1
20170039255 Raj et al. Feb 2017 A1
20170052536 Warner et al. Feb 2017 A1
20170053441 Nadumane et al. Feb 2017 A1
20170063894 Muddu et al. Mar 2017 A1
20170068409 Nair Mar 2017 A1
20170070775 Taxier et al. Mar 2017 A1
20170075984 Deshpande et al. Mar 2017 A1
20170084168 Janchookiat Mar 2017 A1
20170090437 Veeramani et al. Mar 2017 A1
20170092130 Bostick et al. Mar 2017 A1
20170093700 Gilley et al. Mar 2017 A1
20170098086 Hoernecke et al. Apr 2017 A1
20170103327 Penilla et al. Apr 2017 A1
20170103403 Chu et al. Apr 2017 A1
20170123389 Baez et al. May 2017 A1
20170134415 Muddu et al. May 2017 A1
20170177715 Chang et al. Jun 2017 A1
20170180147 Brandman et al. Jun 2017 A1
20170188216 Koskas et al. Jun 2017 A1
20170212482 Boettcher et al. Jul 2017 A1
20170212668 Shah et al. Jul 2017 A1
20170220641 Chi et al. Aug 2017 A1
20170230930 Frey Aug 2017 A1
20170235817 Deodhar et al. Aug 2017 A1
20170251182 Siminoff et al. Aug 2017 A1
20170270124 Nagano et al. Sep 2017 A1
20170277769 Pasupathy et al. Sep 2017 A1
20170278003 Liu Sep 2017 A1
20170294132 Colmenares Oct 2017 A1
20170315522 Kwon et al. Nov 2017 A1
20170315697 Jacobson et al. Nov 2017 A1
20170322534 Sinha et al. Nov 2017 A1
20170323389 Vavrasek Nov 2017 A1
20170329289 Kohn et al. Nov 2017 A1
20170336770 MacMillan Nov 2017 A1
20170345287 Fuller et al. Nov 2017 A1
20170351957 Lecue et al. Dec 2017 A1
20170357225 Asp et al. Dec 2017 A1
20170357490 Park et al. Dec 2017 A1
20170357908 Cabadi et al. Dec 2017 A1
20170372271 Goldsmith et al. Dec 2017 A1
20180005495 Hieb Jan 2018 A1
20180012159 Kozloski et al. Jan 2018 A1
20180013579 Fairweather et al. Jan 2018 A1
20180018508 Tusch Jan 2018 A1
20180024520 Sinha et al. Jan 2018 A1
20180039238 Gärtner et al. Feb 2018 A1
20180048485 Pelton et al. Feb 2018 A1
20180069932 Tiwari et al. Mar 2018 A1
20180114140 Chen et al. Apr 2018 A1
20180137288 Polyakov May 2018 A1
20180157930 Rutschman et al. Jun 2018 A1
20180162400 Abdar Jun 2018 A1
20180176241 Manadhata et al. Jun 2018 A1
20180198627 Mullins Jul 2018 A1
20180203961 Aisu et al. Jul 2018 A1
20180211539 Boss et al. Jul 2018 A1
20180239982 Rutschman et al. Aug 2018 A1
20180268238 Khan et al. Sep 2018 A1
20180275625 Park et al. Sep 2018 A1
20180276962 Butler et al. Sep 2018 A1
20180292797 Lamparter et al. Oct 2018 A1
20180308475 Locke et al. Oct 2018 A1
20180336785 Ghannam et al. Nov 2018 A1
20180356775 Harvey Dec 2018 A1
20180359111 Harvey Dec 2018 A1
20180364654 Locke et al. Dec 2018 A1
20190005025 Malabarba Jan 2019 A1
20190013023 Pourmohammad et al. Jan 2019 A1
20190017719 Sinha et al. Jan 2019 A1
20190025771 Park et al. Jan 2019 A1
20190037135 Hedge Jan 2019 A1
20190042988 Brown et al. Feb 2019 A1
20190065014 Richter et al. Feb 2019 A1
20190088059 Santhosh Mar 2019 A1
20190088106 Grundstrom Mar 2019 A1
20190089808 Santhosh et al. Mar 2019 A1
20190094824 Xie et al. Mar 2019 A1
20190096147 Park et al. Mar 2019 A1
20190096217 Pourmohammad et al. Mar 2019 A1
20190102840 Perl et al. Apr 2019 A1
20190108492 Nelson et al. Apr 2019 A1
20190121801 Jethwa et al. Apr 2019 A1
20190122050 Beals et al. Apr 2019 A1
20190130365 Pell et al. May 2019 A1
20190138333 Deutsch et al. May 2019 A1
20190138512 Pourmohammad et al. May 2019 A1
20190147883 Mellenthin et al. May 2019 A1
20190156601 Sinha et al. May 2019 A1
20190158309 Park et al. May 2019 A1
20190163152 Worrall et al. May 2019 A1
20190172165 Verteletskyi et al. Jun 2019 A1
20190268178 Fairweather et al. Aug 2019 A1
20190310979 Masuzaki et al. Oct 2019 A1
20190342112 Li et al. Nov 2019 A1
20190361852 Rogynskyy et al. Nov 2019 A1
20190377306 Harvey Dec 2019 A1
20200035101 Brooks et al. Jan 2020 A1
20200116505 Lei et al. Apr 2020 A1
20200210906 Rice et al. Jul 2020 A1
20200226156 Borra et al. Jul 2020 A1
20200234523 Ma et al. Jul 2020 A1
20200285203 Thakur et al. Sep 2020 A1
20200336328 Harvey Oct 2020 A1
20200348632 Harvey Nov 2020 A1
20200387576 Brett et al. Dec 2020 A1
20200396208 Brett et al. Dec 2020 A1
20210042299 Migliori Feb 2021 A1
20210043221 Yelchuru et al. Feb 2021 A1
20210325070 Endel et al. Oct 2021 A1
20210342961 Winter et al. Nov 2021 A1
20210381711 Harvey et al. Dec 2021 A1
20210381712 Harvey et al. Dec 2021 A1
20210382445 Harvey et al. Dec 2021 A1
20210383041 Harvey et al. Dec 2021 A1
20210383042 Harvey et al. Dec 2021 A1
20210383200 Harvey et al. Dec 2021 A1
20210383219 Harvey et al. Dec 2021 A1
20210383235 Harvey et al. Dec 2021 A1
20210383236 Harvey et al. Dec 2021 A1
20220066402 Harvey et al. Mar 2022 A1
20220066405 Harvey Mar 2022 A1
20220066432 Harvey et al. Mar 2022 A1
20220066434 Harvey et al. Mar 2022 A1
20220066528 Harvey et al. Mar 2022 A1
20220066722 Harvey et al. Mar 2022 A1
20220066754 Harvey et al. Mar 2022 A1
20220066761 Harvey et al. Mar 2022 A1
20220067226 Harvey et al. Mar 2022 A1
20220067227 Harvey et al. Mar 2022 A1
20220067230 Harvey et al. Mar 2022 A1
20220069863 Harvey et al. Mar 2022 A1
20220070293 Harvey et al. Mar 2022 A1
20220121965 Chatterji et al. Apr 2022 A1
20220138684 Harvey May 2022 A1
20220215264 Harvey et al. Jul 2022 A1
20230010757 Preciado Jan 2023 A1
20230071312 Preciado et al. Mar 2023 A1
20230076011 Preciado et al. Mar 2023 A1
20230083703 Meiners Mar 2023 A1
20230214555 Harvey et al. Jul 2023 A1
Foreign Referenced Citations (46)
Number Date Country
2019226217 Nov 2020 AU
2019226264 Nov 2020 AU
2019351573 May 2021 AU
101415011 Apr 2009 CN
102136099 Jul 2011 CN
102136100 Jul 2011 CN
102650876 Aug 2012 CN
103942308 Jul 2014 CN
104040583 Sep 2014 CN
104603832 May 2015 CN
104919484 Sep 2015 CN
105841293 Aug 2016 CN
106204392 Dec 2016 CN
106406806 Feb 2017 CN
106960269 Jul 2017 CN
107147639 Sep 2017 CN
107598928 Jan 2018 CN
2 528 033 Nov 2012 EP
3 268 821 Jan 2018 EP
3 324 306 May 2018 EP
H10-049552 Feb 1998 JP
2003-162573 Jun 2003 JP
2007-018322 Jan 2007 JP
4073946 Apr 2008 JP
2008-107930 May 2008 JP
2008-310533 Dec 2008 JP
2010-277532 Dec 2010 JP
2013-152618 Aug 2013 JP
2014-044457 Mar 2014 JP
2015-060434 Mar 2015 JP
2016066115 Jun 2016 KR
20160102923 Aug 2016 KR
WO-2009020158 Feb 2009 WO
WO-2011100255 Aug 2011 WO
WO-2013050333 Apr 2013 WO
WO-2015106702 Jul 2015 WO
WO-2015145648 Oct 2015 WO
WO-2017035536 Mar 2017 WO
WO-2017192422 Nov 2017 WO
WO-2017194244 Nov 2017 WO
WO-2017205330 Nov 2017 WO
WO-2017213918 Dec 2017 WO
WO-2018132112 Jul 2018 WO
WO-2018232147 Dec 2018 WO
WO-2020061621 Apr 2020 WO
WO-2022042925 Mar 2022 WO
Non-Patent Literature Citations (79)
Entry
Hipla, “Visitor Management: Smart Solutions to visitors, saving time and maximizing productivity,” URL: https://hipla.io/visitor-management.html, retrieved from internet Mar. 3, 2021, 3 pages.
Siemens, “The office as an active contributor to business success,” URL: https://new.siemens.com/bg/en/products/buildings/markets/smart-office.html, retrieved from internet Mar. 4, 2021, 12 pages.
Splan, “Visitor Management: Manage visitor registrations and check-ins in an efficient and secured manner.” URL: https://www.splan.com/visitor-management-system.html, 11 pages.
International Search Report and Written Opinion on PCT/US2017/013647, dated Apr. 18, 2017, 10 pages.
Marr, B., “What Is Digital Twin Technology—and Why Is It So Important?”, Enterprise Tech, Mar. 6, 2017 (5 pages).
Balaji et al, “Brick: Metadata schema for portable smart building applications,” Applied Energy, 2018 (20 pages).
Balaji et al, “Brick: Metadata schema for portable smart building applications,” Applied Energy, Sep. 15, 2018, 3 pages, (Abstract).
Balaji et al, “Demo Abstract: Portable Queries Using the Brick Schema for Building Applications,” BuildSys '16, Palo Alto, CA, USA, Nov. 16-17, 2016 (2 pages).
Balaji, B. et al., “Brick: Towards a Unified Metadata Schema for Buildings.” BuildSys '16, Palo Alto, CA, USA, Nov. 16-17, 2016 (10 pages).
Bhattacharya et al., “Short Paper: Analyzing Metadata Schemas for Buildings —The Good, the Bad and the Ugly,” BuildSys '15, Seoul, South Korea, Nov. 4-5, 2015 (4 pages).
Bhattacharya, A., “Enabling Scalable Smart-Building Analytics,” Electrical Engineering and Computer Sciences, University of California at Berkeley, Technical Report No. UCB/EECS-2016-201, Dec. 15, 2016 (121 pages).
Brick, “Brick Schema: Building Blocks for Smart Buildings,” URL: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.memoori.com/wp-content/uploads/2016/06/Brick_Schema_Whitepaper.pdf, Mar. 2019 (17 pages).
Brick, “Brick: Towards a Unified Metadata Schema for Buildings,” URL: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://brickschema.org/papers/Brick_BuildSys_Presentation.pdf, Presented at BuildSys '16, Nov. 2016 (46 pages).
Brick, “Metadata Schema for Buildings,” URL: https://brickschema.org/docs/Brick-Leaflet.pdf, retrieved from internet Dec. 24, 2019 (3 pages).
Chinese Office Action on CN Appl. No. 201780003995.9 dated Apr. 8, 2021 (21 pages with English language translation).
Chinese Office action on CN Appl. No. 201780043400.2 dated Apr. 25, 2021 (15 pages with English language translation).
Curry, E. et al., “Linking building data in the cloud: Integrating cross-domain building data using linked data.” Advanced Engineering Informatics, 2013, 27 (pp. 206-219).
Digital Platform Litigation Documents Part 1, includes cover letter, dismissal of case DDE-1-21-cv-01796, IPR2023-00022 (documents filed Jan. 26, 2023-Oct. 7, 2022), and IPR2023-00085 (documents filed Jan. 26, 2023-Oct. 20, 2022) (748 pages total).
Digital Platform Litigation Documents Part 10, includes DDE-1-21-cv-01796 (documents filed Nov. 1, 2022-Dec. 22, 2021 (1795 pages total).
Digital Platform Litigation Documents Part 2, includes IPR2023-00085 (documents filed Oct. 20, 2022) (172 pages total).
Digital Platform Litigation Documents Part 3, includes IPR2023-00085 (documents filed Oct. 20, 2022) and IPR2023-00170 (documents filed Nov. 28, 2022-Nov. 7, 2022) (397 pages total).
Digital Platform Litigation Documents Part 4, includes IPR2023-00170 (documents filed Nov. 7, 2022) and IPR2023-00217 (documents filed Jan. 18, 2023-Nov. 15, 2022) (434 pages total).
Digital Platform Litigation Documents Part 5, includes IPR2023-00217 (documents filed Nov. 15, 2022) and IPR2023-00257 (documents filed Jan. 25, 2023-Nov. 23, 2022) (316 pages total).
Digital Platform Litigation Documents Part 6, includes IPR2023-00257 (documents filed Nov. 23, 2022) and IPR 2023-00346 (documents filed Jan. 3, 2023-Dec. 13, 2022) (295 pages total).
Digital Platform Litigation Documents Part 7, includes IPR 2023-00346 (documents filed Dec. 13, 2022) and IPR2023-00347 (documents filed Jan. 3, 2023-Dec. 13, 2022) (217 pages total).
Digital Platform Litigation Documents Part 8, includes IPR2023-00347 (documents filed Dec. 13, 2022), EDTX-2-22-cv-00243 (documents filed Sep. 20, 2022-Jun. 29, 2022), and DDE-1-21-cv-01796 (documents filed Feb. 3, 2023-Jan. 10, 2023 (480 pages total).
Digital Platform Litigation Documents Part 9, includes DDE-1-21-cv-01796 (documents filed Jan. 10, 2023-Nov. 1, 2022 (203 pages total).
El Kaed, C. et al., “Building management insights driven by a multi-system semantic representation approach,” 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), Dec. 12-14, 2016, (pp. 520-525).
Ellis, C. et al., “Creating a room connectivity graph of a building from per-room sensor units.” BuildSys '12, Toronto, ON, Canada, Nov. 6, 2012 (7 pages).
Extended European Search Report on EP Application No. 18196948.6 dated Apr. 10, 2019 (9 pages).
Fierro et al., “Beyond a House of Sticks: Formalizing Metadata Tags with Brick,” BuildSys '19, New York, NY, USA, Nov. 13-14, 2019 (10 pages).
Fierro et al., “Dataset: An Open Dataset and Collection Tool for BMS Point Labels,” DATA'19, New York, NY, USA, Nov. 10, 2019 (3 pages).
Fierro et al., “Design and Analysis of a Query Processor for Brick,” ACM Transactions on Sensor Networks, Jan. 2018, vol. 1, No. 1, art. 1 (25 pages).
Fierro et al., “Design and Analysis of a Query Processor for Brick,” BuildSys '17, Delft, Netherlands, Nov. 8-9, 2017 (10 pages).
Fierro et al., “Mortar: An Open Testbed for Portable Building Analytics,” BuildSys '18, Shenzhen, China, Nov. 7-8, 2018 (10 pages).
Fierro et al., “Why Brick is a Game Changer for Smart Buildings,” URL: https://brickschema.org/papers/Brick_Memoori_Webinar_Presentation.pdf, Memoori Webinar, 2019 (67 pages).
Fierro, “Writing Portable Building Analytics with the Brick Metadata Schema,” UC Berkeley, ACM E-Energy, 2019 (39 pages).
Fierro, G., “Design of an Effective Ontology and Query Processor Enabling Portable Building Applications,” Electrical Engineering and Computer Sciences, University of California at Berkeley, Technical Report No. UCB/EECS-2019-106, Jun. 27, 2019 (118 pages).
File History for U.S. Appl. No. 12/776,159, filed May 7, 2010 (722 pages).
Final Conference Program, ACM BuildSys 2016, Stanford, CA, USA, Nov. 15-17, 2016 (7 pages).
Gao et al., “A large-scale evaluation of automated metadata inference approaches on sensors from air handling units,” Advanced Engineering Informatics, 2018, 37 (pp. 14-30).
Harvey, T., “Quantum Part 3: The Tools of Autonomy, How PassiveLogic's Quantum Creator and Autonomy Studio software works,” URL: https://www.automatedbuildings.com/news/jan22/articles/passive/211224010000passive.html, Jan. 2022 (7 pages).
Harvey, T., “Quantum: The Digital Twin Standard for Buildings,” URL: https://www.automatedbuildings.com/news/feb21/articles/passivelogic/210127124501passivelogic.html, Feb. 2021 (6 pages).
Hu, S et al., “Building performance optimisation: A hybrid architecture for the integration of contextual information and time-series data,” Automation in Construction, 2016, 70 (pp. 51-61).
International Search Report and Written Opinion for PCT Appl. Ser. No. PCT/US2017/013831 dated Mar. 31, 2017 (14 pages).
International Search Report and Written Opinion for PCT Appl. Ser. No. PCT/US2017/035524 dated Jul. 24, 2017 (14 pages).
International Search Report and Written Opinion on PCT/US2017/052060, dated Oct. 5, 2017, 11 pages.
International Search Report and Written Opinion on PCT/US2017/052633, dated Oct. 23, 2017, 9 pages.
International Search Report and Written Opinion on PCT/US2017/052829, dated Nov. 27, 2017, 24 pages.
International Search Report and Written Opinion on PCT/US2018/024068, dated Jun. 15, 2018, 22 pages.
International Search Report and Written Opinion on PCT/US2018/052971, dated Mar. 1, 2019, 19 pages.
International Search Report and Written Opinion on PCT/US2018/052974, dated Dec. 19, 2018, 13 pages.
International Search Report and Written Opinion on PCT/US2018/052975, dated Jan. 2, 2019, 13 pages.
International Search Report and Written Opinion on PCT/US2018/052994, dated Jan. 7, 2019, 15 pages.
International Search Report and Written Opinion on PCT/US2019/015481, dated May 17, 2019, 78 pages.
International Search Report and Written Opinion on PCT/US2020/058381, dated Jan. 27, 2021, 30 pages.
Japanese Office Action on JP Appl. No. 2018-534963 dated May 11, 2021 (16 pages with English language translation).
Koh et al., “Plaster: An Integration, Benchmark, and Development Framework for Metadata Normalization Methods,” BuildSys '18, Shenzhen, China, Nov. 7-8, 2018 (10 pages).
Koh et al., “Scrabble: Transferrable Semi-Automated Semantic Metadata Normalization using Intermediate Representation,” BuildSys '18, Shenzhen, China, Nov. 7-8, 2018 (10 pages).
Koh et al., “Who can Access What, and When?” BuildSys '19, New York, NY, USA, Nov. 13- 14, 2019 (4 pages).
Li et al., “Event Stream Processing with Out-of-Order Data Arrival,” International Conferences on Distributed Computing Systems, 2007, (8 pages).
Nissin Electric Co., Ltd., “Smart power supply system (SPSS),” Outline of the scale verification plan, Nissin Electric Technical Report, Japan, Apr. 23, 2014, vol. 59, No. 1 (23 pages).
Passivelogic, “Explorer: Digital Twin Standard for Autonomous Systems. Made interactive.” URL: https://passivelogic.com/software/quantum-explorer/, retrieved from internet Jan. 4, 2023 (13 pages).
Passivelogic, “Quantum: The Digital Twin Standard for Autonomous Systems, A physics-based ontology for next-generation control and AI.” URL: https://passivelogic.com/software/quantum-standard/, retrieved from internet Jan. 4, 2023 (20 pages).
Quantum Alliance, “Quantum Explorer Walkthrough,” 2022, (7 pages) (screenshots from video).
Results of the Partial International Search for PCT/US2018/052971, dated Jan. 3, 2019, 3 pages.
Sinha, Sudhi and Al Huraimel, Khaled, “Reimagining Businesses with AI” John Wiley & Sons, Inc., Hoboken, NJ, USA, 2021 (156 pages).
Sinha, Sudhi R. and Park, Youngchoon, “Building an Effective IoT Ecosystem for Your Business,” Johnson Controls International, Springer International Publishing, 2017 (286 pages).
Sinha, Sudhi, “Making Big Data Work for Your Business: A guide to effective Big Data analytics,” Impackt Publishing LTD., Birmingham, UK, Oct. 2014 (170 pages).
The Virtual Nuclear Tourist, “Calvert Cliffs Nuclear Power Plant,” URL: http://www.nucleartourist.com/us/calvert.htm, Jan. 11, 2006 (2 pages).
University of California at Berkeley, EECS Department, “Enabling Scalable Smart-Building Analytics,” URL: https://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-201.html, retrieved from internet Feb. 15, 2022 (7 pages).
Van Hoof, Bert, “Announcing Azure Digital Twins: Create digital replicas of spaces and infrastructure using cloud, AI and IoT,” URL: https://azure.microsoft.com/en-us/blog/announcing-azure-digital-twins-create-digital-replicas-of-spaces-and-infrastructure-using-cloud-ai-and-iot/, Sep. 24, 2018 (11 pages).
W3C, “SPARQL: Query Language for RDF,” located on the Wayback Machine, URL: https://web.archive.org/web/20161230061728/http://www.w3.org/TR/rdf-sparql-query/), retrieved from internet Nov. 15, 2022 (89 pages).
Wei et al., “Development and Implementation of Software Gateways of Fire Fighting Subsystem Running on EBI,” Control, Automation and Systems Engineering, IITA International Conference on, IEEE, Jul. 2009 (pp. 9-12).
White et al., “Reduce building maintenance costs with AWS IoT TwinMaker Knowledge Graph,” The Internet of Things on AWS—Official Blog, URL: https://aws.amazon.com/blogs/iot/reduce-building-maintenance-costs-with-aws-iot-twinmaker-knowledge-graph/, Nov. 18, 2022 (10 pages).
Zhou, Q. et al., “Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams,” Further Generation Computer Systems, 2017, 76 (pp. 391-406).
U.S. Appl. No. 17/566,029, Passivelogic, Inc.
U.S. Appl. No. 17/567,275, Passivelogic, Inc.
U.S. Appl. No. 17/722,115, Passivelogic, Inc.
Related Publications (1)
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62794407 Jan 2019 US
62794393 Jan 2019 US
62794502 Jan 2019 US
62794389 Jan 2019 US
62794348 Jan 2019 US
62794535 Jan 2019 US