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.
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.
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.
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
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
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
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
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
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
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
In various embodiments, smart lobby system 1100 implements and/or facilitates various features 1101 (e.g., as shown in
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
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
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
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
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.
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.
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 |
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 |
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. |
Number | Date | Country | |
---|---|---|---|
20200234523 A1 | Jul 2020 | US |
Number | Date | Country | |
---|---|---|---|
62794489 | Jan 2019 | US | |
62794276 | Jan 2019 | US | |
62794533 | Jan 2019 | US | |
62794032 | Jan 2019 | US | |
62794357 | Jan 2019 | US | |
62794370 | Jan 2019 | US | |
62794415 | Jan 2019 | US | |
62794407 | Jan 2019 | US | |
62794393 | Jan 2019 | US | |
62794502 | Jan 2019 | US | |
62794389 | Jan 2019 | US | |
62794348 | Jan 2019 | US | |
62794535 | Jan 2019 | US |