Building management system with auto-configuration using existing points

Description

BACKGROUND

The present disclosure relates generally to the field of building management platforms such as a building management system. Specifically, the present disclosure relates to systems and methods for self-configuring and commissioning spaces such as buildings. Building management platforms may include entities such as controllers, sensors, or water heaters deployed within a space. Initial commissioning of new buildings and/or spaces thereof can be time-intensive and involve significant expense.

SUMMARY

One implementation of the present disclosure is a method for visualizing a building model comprising retrieving an identifier associated with a digital representation of a building asset from a digital representation representing at least a portion of a building, retrieving, using the identifier, a graphical representation of the asset from a building model having a different format than the digital representation, and rendering the graphical representation on a display device.

In various embodiments, the asset includes at least one of a space or a piece of equipment associated with the portion of the building. In various embodiments, the graphical representation of the asset is associated with a simple vector format (SVF) file. In various embodiments, the building model includes a graph data structure. In various embodiments, the building model includes a building information management (BIM) model. In various embodiments, the graphical representation of the asset is a three-dimensional representation. In various embodiments, rending the graphical representation includes displaying the graphical representation in an interface of a building management system (BMS). In various embodiments, the graphical representation of the asset includes at least one image of the asset.

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 processor to retrieve an identifier associated with a digital representation of a building asset from a digital representation representing at least a portion of a building, retrieve, using the identifier, a graphical representation of the asset from a building model having a different format than the digital representation, and render the graphical representation on a display device.

Another implementation of the present disclosure is a system for visualizing a building model comprising a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to retrieve an identifier associated with a digital representation of a building asset from a digital representation representing at least a portion of a building, retrieve, using the identifier, a graphical representation of the asset from a building model having a different format than the digital representation, and render the graphical representation on a display device.

Another implementation of the present disclosure is a method of labeling building data comprising receiving, by a labeling system, a plurality of strings relating to data associated with a building, wherein the plurality of strings are received from one or more building devices, segregating the plurality of strings based on at least one semantic element included in the plurality of strings, clustering the plurality of strings based on at least one related element shared by at least a portion of the plurality of strings, labeling at least some of the plurality of strings based on the segregated and clustered plurality of strings, and generating a digital representation of the building using the labeled strings.

In various embodiments, labeling the at least some of the plurality of strings includes generating at least one of a class, a location, or an identifier associated with each of the at least some of the plurality of strings. In various embodiments, the method further comprises executing one or more predefined operators, wherein the one or more predefined operators include a series of actions. In various embodiments, labeling the at least some of the plurality of strings includes executing a machine learning algorithm to suggest labels associated with the at least some of the plurality of strings. In various embodiments, the method further comprises interacting with an external system using at least one of an application programming interface (API) or a software development kit (SDK). In various embodiments, the external system is a parsing system configured to parse at least one of the plurality of strings to extract a semantic element. In various embodiments, the external system is a lookup system configured to generate supplemental data associated with at least one of the plurality of strings. In various embodiments, labeling the at least some of the plurality of strings includes identifying information associated with one or more building assets from the plurality of strings.

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 receive a plurality of strings relating to data associated with a building, wherein the plurality of strings are received from one or more building devices, segregate the plurality of strings based on at least one semantic element included in the plurality of strings, cluster the plurality of strings based on at least one related element shared by at least a portion of the plurality of strings, label at least some of the plurality of strings based on the segregated and clustered plurality of strings, and generate a digital representation of the building using the labeled strings.

In various embodiments, labeling the at least some of the plurality of strings includes generating at least one of a class, a location, or an identifier associated with each of the at least some of the plurality of strings. In various embodiments, the one or more processors are further configured to execute one or more predefined operators, wherein the one or more predefined operators include a series of actions. In various embodiments, labeling the at least some of the plurality of strings includes executing a machine learning algorithm to suggest labels associated with the at least some of the plurality of strings. In various embodiments, the one or more processors are further configured to interact with an external system using at least one of an application programming interface (API) or a software development kit (SDK). In various embodiments, the external system is a parsing system configured to parse at least one of the plurality of strings to extract a semantic element. In various embodiments, the external system is a lookup system configured to generate supplemental data associated with at least one of the plurality of strings. In various embodiments, labeling the at least some of the plurality of strings includes identifying information associated with one or more building assets from the plurality of strings.

Another implementation of the present disclosure is a data preprocessing and refinement tool configured to receive a plurality of strings relating to data associated with a building, wherein the plurality of strings are received from one or more building devices, segregate the plurality of strings based on at least one semantic element included in the plurality of strings, cluster the plurality of strings based on at least one related element shared by at least a portion of the plurality of strings, label at least some of the plurality of strings based on the segregated and clustered plurality of strings, and generate a digital representation of the building using the labeled strings.

Another implementation of the present disclosure is a method for commissioning a building comprising identifying a building asset within a digital representation of a building having one or more parameters describing relationships of the building asset within the building, controlling one or more other building assets included within the digital representation to change an environmental parameter of the building, monitoring a response of at least one of the building asset or one of the one or more other building assets to the change in the environmental parameter, generating a parameter for the digital representation associated with the building asset based on the monitoring, and updating the digital representation using the parameter.

In various embodiments, the parameter describes a confidence level in a validity of one of the one or more parameters. In various embodiments, the method further comprises updating at least one of the one or more parameters using the parameter. In various embodiments, generating the parameter includes analyzing a covariance between sensor measurements of the one or more other building assets associated with the change in the environmental parameter of the building. In various embodiments, the digital representation includes a graph data structure and wherein the method further comprises updating one or more portions of the graph data structure to represent a relationship of the building asset. In various embodiments, the digital representation includes a graph data structure and wherein the method further comprises comparing versions of the graph data structure representing at least a portion of the building to identify changes between the versions. In various embodiments, identifying the building asset includes selecting an asset based on an identified change between the versions of the graph data structure.

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 identify a building asset within a digital representation of a building having one or more parameters describing relationships of the building asset within the building, control one or more other building assets included within the digital representation to change an environmental parameter of the building, monitor a response of at least one of the building asset or one of the one or more other building assets to the change in the environmental parameter, generate a parameter for the digital representation associated with the building asset based on the monitoring, and update the digital representation using the parameter.

In various embodiments, the parameter describes a confidence level in a validity of one of the one or more parameters. In various embodiments, the one or more processors are further configured to update at least one of the one or more parameters using the parameter. In various embodiments, generating the parameter includes analyzing a covariance between sensor measurements of the one or more other building assets associated with the change in the environmental parameter of the building. In various embodiments, the digital representation includes a graph data structure and wherein the one or more processors are further configured to update one or more portions of the graph data structure to represent a relationship of the building asset. In various embodiments, the digital representation includes a graph data structure and wherein the one or more processors are further configured to compare versions of the graph data structure representing at least a portion of the building to identify changes between the versions. In various embodiments, identifying the building asset includes selecting an asset based on an identified change between the versions of the graph data structure.

Another implementation of the present disclosure is a building management system (BMS) comprising a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to identify a building asset within a digital representation of a building having one or more parameters describing relationships of the building asset within the building, control one or more other building assets included within the digital representation to change an environmental parameter of the building, monitor a response of at least one of the building asset or one of the one or more other building assets to the change in the environmental parameter, generate a parameter for the digital representation associated with the building asset based on the monitoring, and update the digital representation using the parameter.

In various embodiments, the parameter describes a confidence level in a validity of one of the one or more parameters. In various embodiments, the processing circuit is further configured to update at least one of the one or more parameters using the parameter. In various embodiments, generating the parameter includes analyzing a covariance between sensor measurements of the one or more other building assets associated with the change in the environmental parameter of the building. In various embodiments, the digital representation includes a graph data structure and wherein the processing circuit is further configured to update one or more portions of the graph data structure to represent a relationship of the building asset. In various embodiments, the digital representation includes a graph data structure and wherein the processing circuit is further configured to compare versions of the graph data structure representing at least a portion of the building to identify changes between the versions, and wherein identifying the building asset includes selecting an asset based on an identified change between the versions of the graph data structure.

Another implementation of the present disclosure is a method for generating a graph data structure comprising receiving, by one or more processors, data associated with a building, generating one or more space nodes in the graph data structure corresponding to spaces within the building based on the data, generating one or more asset nodes in the graph data structure corresponding to assets within the building based on the data, associating sensor data with the one or more asset nodes based on the data, classifying the sensor data based on the data, and generating a relationship between at least two of the one or more space nodes, the one or more asset nodes, and the classified sensor data.

In various embodiments, the data includes building information model (BIM) data. In various embodiments, generating the one or more space nodes includes extracting an identifier from the data associated with the building and storing the identifier with at least one of the one or more spaces nodes. In various embodiments, generating the one or more asset nodes includes extracting an identifier from the data associated with the building and storing the identifier with at least one of the one or more asset nodes. In various embodiments, associating the sensor data with the one or more asset nodes includes dynamically controlling an environmental variable of the building, monitoring sensor measurements, and associating the sensor data based on the monitoring. In various embodiments, classifying the sensor data includes assigning a tag to the sensor data wherein the tag is associated with at least one of a source of the sensor data or a purpose of the sensor data. In various embodiments, the data includes string values and wherein generating the relationship includes parsing the string values to identify a semantic representation associating at least two of a space of the spaces, an asset of the assets, and a value of the classified sensor data. In various embodiments, associating the sensor data with the one or more asset nodes includes generating one or more data nodes and linking the one or more data nodes with at least one of the one or more asset nodes.

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 receive data associated with a building, generate one or more space nodes in a graph data structure corresponding to spaces within the building based on the data, generate one or more asset nodes in the graph data structure corresponding to assets within the building based on the data, associate sensor data with the one or more asset nodes based on the data, classify the sensor data based on the data, and generate a relationship between at least two of the one or more space nodes, the one or more asset nodes, and the classified sensor data.

Another implementation of the present disclosure is a system for generating a graph data structure comprising a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to receive data associated with a building, generate one or more space nodes in the graph data structure corresponding to spaces within the building based on the data, generate one or more asset nodes in the graph data structure corresponding to assets within the building based on the data, associate sensor data with the one or more asset nodes based on the data, classify the sensor data based on the data, and generate a relationship between at least two of the one or more space nodes, the one or more asset nodes, and the classified sensor data.

Another implementation of the present disclosure is a method for augmenting a building model comprising receiving a first building model associated with at least a portion of a building and having a first format, receiving a second building modeling associated with the portion of the building and having a second format, comparing the first building model to the second building model to identify at least one element in the first building model not included in the second building model, and updating the second building model to include the identified at least one element.

In various embodiments, the second building model is a building information management (BIM) model. In various embodiments, the second format is an industry foundation classes (IFC) file format. In various embodiments, the first format is a first version of a building model and the second format is a second version of the building model and wherein updating the second building model to include the identified at least one element includes updating the second version of the building model with information from the first version of the building model. In various embodiments, updating the second building model includes generating one or more data objects within the second building model according to the second format. In various embodiments, updating the second building model includes translating information from the first format of the first building model into the second format of the second building model. In various embodiments, updating the second building model includes adding an identifier from the first building model to the second building model.

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 receive a first building model associated with at least a portion of a building and having a first format, receive a second building modeling associated with the portion of the building and having a second format, compare the first building model to the second building model to identify at least one element in the first building model not included in the second building model, and update the second building model to include the identified at least one element.

Another implementation of the present disclosure is a system for augmenting a building model comprising a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to receive a first building model associated with at least a portion of a building and having a first format, receive a second building modeling associated with the portion of the building and having a second format, compare the first building model to the second building model to identify at least one element in the first building model not included in the second building model, and update the second building model to include the identified at least one element.

BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

FIG. 2A is a block diagram of an entity graph, according to an exemplary embodiment.

FIG. 2B is another block diagram of the entity graph of FIG. 2A, according to an exemplary embodiment.

FIG. 3 is a block diagram illustrating a smart configuration and commissioning system, according to some exemplary embodiments;

FIG. 4 is a flow diagram illustrating a method of generating an entity graph, according to some exemplary embodiments;

FIG. 5 is a flow diagram illustrating a method of classifying input strings, according to some exemplary embodiments;

FIGS. 6A-B is a diagram illustrating input string manipulation, according to some exemplary embodiments;

FIG. 7 is diagram illustrating auto-commissioning a space, according to some exemplary embodiments;

FIG. 8 is a diagram illustrating digesting information into an entity graph, according to some exemplary embodiments;

FIG. 9 is a diagram illustrating augmenting an entity graph with additional information, according to some exemplary embodiments.

DETAILED DESCRIPTION

Hereinafter, example embodiments will be described in more detail with reference to the accompanying drawings.

In various embodiments, building management platforms such as a building management system includes digital representations of physical spaces. For example, a building management system may include a digital twin of a building that represents the people, places, assets, and events within the building. In various embodiments, digital twins are generated during the construction of a building. For example, an architect may generate a building information model (BIM) that is used by builders to construct a building and service personnel to maintain the building. Additionally or alternatively, digital representations such as digital twins may be generated for existing buildings. For example, a building management system of an existing building may generate a digital representation of the building. However, it may be difficult to generate a digital representation of a building (new, existing, or otherwise). For example, manually identifying the assets in a building may be difficult and time consuming. As a further example, some sources of existing building data may not be easily translatable to a digital representation and may require manual transfer. Further, configuring the devices of the buildings/spaces to work properly with one another and with the building management system can be difficult and time-consuming as well. In general, it may be desirable to avoid manually generating digital representations of buildings (e.g., to reduce input errors, because of the amount of data to be input, etc.). Therefore, systems and methods of self-configuring and commissioning spaces are needed. Specifically, system and methods to facilitate ingesting building data, generating a digital representation, and deriving new information based on the digital representation are needed. Virtual representations, digital representations, smart entities, and digital twins are used interchangeably in the present disclosure; in other words, where the present disclosure refers to virtual representations or smart entities, it should be understood that the features discussed could be implemented using digital twins or any other digital counterpart (e.g., updated in real-time, near real-time, periodically, in response to events, or in any other fashion). Additionally, in various embodiments, the systems and methods of the present disclosure can utilize digital twins for higher-level entities (e.g., such as buildings or campuses that include or are otherwise related to particular spaces, assets such as building equipment, people, and/or events) and/or for lower-level entities, such as digital twins of particular spaces (e.g., rooms, floors, etc.), people, assets (e.g., particular pieces of building equipment or groups of building equipment), and events. All such implementations are contemplated within the scope of the present disclosure.

One solution may include a smart configuration and commissioning system. The smart configuration and commissioning system may automate the process of generating and deploying a digital representation of a building, thereby reducing or eliminating a need for manual intervention. In various embodiments, the smart configuration and commissioning system may ingest and interpret external data sources (e.g., BIM data, enterprise management data, personnel data, etc.), extract semantic information from external data sources, dynamically determine configuration information, automatically enrich digital representations with derived information, and/or generate digital representations for spaces. In some implementations, the digital representations may be used to configuration and/or control of the building equipment and/or other assets contained in the buildings/spaces, and may significantly lower the time and expense of commissioning and configuring new buildings and spaces.

Referring now to FIGS. 1A-1B, a block diagram of a smart building environment 100 is shown, according to an exemplary embodiment. Smart building environment 100 is shown to include cloud building management platform 140. Cloud building management platform 140 may be configured to collect information from a variety of different data sources. Cloud management platform 140 may create digital representations, referred to as “digital twins,” of physical spaces, equipment, people, and/or events based on the collected information. In various embodiments, the digital representations are stored in an entity graph. In brief overview, an entity graph is a data structure representing entities (e.g., spaces, equipment, people, events, etc.) and relationships between the entities. In various embodiments, the entity graph data structure facilitates advanced artificial intelligence and machine learning associated with the entities. In various embodiments, entities within the entity graph data structure include or are associated with “agents,” or software entities configured to take actions with respect to the digital twins/real world entities with which they are associated. In some implementations, the agents may be configured to implement artificial intelligence/machine learning methodologies. The agents may be configured to facilitate communication and collection of information between the variety of different data sources. Each of the data sources may be implemented as, include, or otherwise use respective agents for facilitating communication amongst or between the data sources and cloud building management platform 140.

In various embodiments, cloud building management platform 140 collects data from buildings 10. For example, cloud building management platform 140 may collect data from buildings 10 such as a school, a hospital, a factory, an office building, and/or the like. It should be understood that the present disclosure is not limited to the number or types of buildings 10 shown in FIG. 1B. As new devices/components/spaces/buildings/events/control loops are added or otherwise incorporated into smart building environment 100, new digital representations (and associated agents, etc.) may be dynamically generated and incorporated into the entity graph data structure.

Buildings 10 may include entities 12. Entities 12 may include spaces, equipment, people, and/or events. In some embodiments, entities 12 include spaces such as floors, rooms, zones, campuses, buildings, and the like. In some embodiments, entities 12 include people such as employees, visitors, pedestrians, staff, and the like. In some embodiments, entities 12 include equipment such as inventory, assets, furniture, vehicles, building components, devices, and the like. For example, entities 12 may include devices such as internet of things (IoT) devices. IoT devices may include any of a variety of physical devices, sensors, actuators, electronics, vehicles, home appliances, and/or other items capable of communicating data over an electronic network (e.g., smart lights, smart appliances, smart home hub devices, etc.). In some embodiments, entities 12 include events such as meetings, fault indications, alarms, and the like. In various embodiments, cloud building management platform 140 receives information associated with buildings 10 and/or entities 12 and generates entity graph 170 based on the received information. Entity graph 170 may include digital twins that are digital representations of real world spaces, equipment, people, events, and/or the like. Entity graph 170 is described in greater detail below with reference to FIG. 2A-2B.

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

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

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

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

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

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

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

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

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

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

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

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

Entity graph circuit 150 may be configured to manage entity graph 170. In various embodiments, entity graph circuit 150 registers and manages various buildings (e.g., building 10, etc.), spaces, persons, subsystems (e.g., building subsystems 120, etc.), devices (e.g., IoT devices, etc.), events, and/or other entities in cloud building management platform 140. As described above, an entity may be any person, place, space, physical object, equipment, or the like. Further, an entity may be any event, data point, record structure, or the like. Entities and entity graph 170 are described in detail below with reference to FIGS. 2A-2B.

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 FIGS. 2A-2B, an entity graph 200 is shown in greater detail, according to an exemplary embodiment. In brief overview, entity graphs such as entity graph 170 and/or entity graph 200 are structured data stored in memory (e.g., a database, memory 146, etc.). Entity graphs such as entity graph 200 and/or entity graph 170 may include digital twins. Digital twins may be digital representations of real world spaces, equipment, people, and/or events. In various embodiments, digital twins represent buildings, building equipment, people associated with buildings, and/or events associated with buildings (e.g., buildings 10, etc.). An entity graph may include nodes and edges, where each node of the entity graph represents an entity and each edge is directed (e.g., from a first node to a second node) and represents a relationship between entities (e.g., indicates that the entity represented by the first node has a particular relationship with the entity represented by the second node). For example, an entity graph may be used to represent a digital twin of a person.

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

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

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

In various embodiments, the entity graph includes data from various sources. For example, the entity graph may include data associated with people, places, assets, and/or the like. In various embodiments, the data source(s) represent a heterogenous source data schema such as an open source common data model (e.g., a Brick Schema/extensions, etc.).

In various embodiments, entity graph includes digital twins and/or context information. A digital twin is a digital representation of spaces, assets, people, events, and/or anything associated with a building or operation thereof. In various embodiments, digital twins are modeled in the entity graph. In various embodiments, digital twins include an active compute process. For example, a digital twin may communicate with other digital twins to sense, predict and act. In various embodiments, digital twins are generated dynamically. For example, a digital twin 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 twins and/or the entity graph include context information. Context information may include real-time data and a historical record of each system in the environment (e.g., campus, building, facility, space, etc.). Context information may be stored in the entity graph. In various embodiments, context information facilitates flexible data modeling for advanced analytics and AI application in scenarios that model highly interconnected entities.

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 200 includes entities 202-258 (stored as nodes within entity graph 200) describing spaces, equipment, events, and people (e.g., business employees, etc.). In various embodiments, entities 202-258 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 200 (e.g., agent entities, etc.). Furthermore, relationships are shown between entities 202-258 directionally describing relationships between two of entities 202-258 (stored as edges within entity graph 200). In various embodiments, cloud building management platform 140 may traverse entity graph 200 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 200 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 224) has a location (e.g., the space represented by entity 202). Furthermore, a second edge “contains” from entity 202 to entity 224 indicates that the location (e.g., the space represented by entity 202) includes the device (e.g., the smart TV represented by entity 224). In some embodiments, entity graph circuit 150 generates the nodes of entity graph 200 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 200. 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 200 representing the card reader controller operation of the door lock.

Smart Configuration and Commissioning System

Referring now to FIG. 3, smart configuration and commissioning system 300 is shown, according to an exemplary embodiment. Smart configuration and commissioning system 300 may automate a process of generating a digital representation of a space such as a building. In various embodiments, smart configuration and commissioning system 300 facilitates generating entity graph 170. Additionally or alternatively, smart configuration and commissioning system 300 may facilitate linking various models and/or subsystems. For example, smart configuration and commissioning system 300 may link a graph data structure to building subsystems such as mapping building spaces to an API of a lighting subsystem. In various embodiments, smart configuration and commissioning system 300 is configured to generate virtual environments for spaces. For example, smart configuration and commissioning system 300 may generate a floor plan, images of assets, spaces (e.g., from structured data such as a BIM model, etc.), 3D assets, and/or the like. In various implementations, smart configuration and commissioning system 300 communicates with various systems, databases, and/or the like over network 340. For example, smart configuration and commissioning system 300 may communicate with building management system 102, cloud building management platform 140, external system 350, user devices 360, and/or deployed device 370. Network 340 may include computer networks such as the Internet, local, wide, metro or other area networks, intranets, satellite networks, other computer networks such as voice or data mobile phone communication networks, combinations thereof, or any other type of electronic communications network. In various embodiments, network 340 facilitates secure communication between smart configuration and commissioning system 300 and other systems. As a non-limiting example, network 340 may implement transport layer security (TLS), secure sockets layer (SSL), hypertext transfer protocol secure (HTTPS), and/or any other secure communication protocol.

External systems 350 may be an external source of data for smart configuration and commissioning system 300. For example, external systems 350 may include a human-resources (HR) database, an email directory, a security system, a DHCP server, a lighting control system, a visitor registration system, a parking management system, and/or the like. External systems 350 may include one or more storage mediums. The storage mediums may include but are not limited to magnetic storage, optical storage, flash storage, and/or RAM. External systems 350 may implement or facilitate various APIs to perform database functions (i.e., managing data stored in external systems 350). The APIs can be but are not limited to SQL, ODBC, JDBC, and/or any other data storage and manipulation API.

In various embodiments, external systems 350 include building model database 352. Building model database 352 may include building models describing one or more buildings and/or spaces. For example, building model database 352 may include a BIM model, an IFC file, a Revit model, a csv file, a COBie file, an RDF graph model, a TTL file, an SVG file, and/or the like. In various embodiments, building model database 352 includes data to be integrated into a digital representation of a building such as entity graph 170. For example, smart configuration and commissioning system 300 may receive a csv file including building assets for a building and may ingest the building assets into a digital representation of the building.

User devices 360 may be a source of data for smart configuration and commissioning system 300. User devices 360 may include smartphones, tablets, handheld devices, scanners, and/or the like. In various embodiments, user devices 360 are configured to provide information to smart configuration and commissioning system 300 to be integrated into a digital representation of a building. For example, a smartphone may capture LIDAR data for a space within a building and smart configuration and commissioning system 300 may integrate the LIDAR data into a digital representation of the building (e.g., to facilitate modeling and/or viewing an interior of the space, etc.). In some embodiments, user devices 360 provide user feedback data. For example, smart configuration and commissioning system 300 may operate a fan that has not been mapped to a space within a building and may receive feedback (e.g., user feedback via user devices 360 and/or sensor feedback indicating that a specific room suddenly became cold) and may determine that the fan serves the specific room. Smart configuration and commissioning system 300 may receive image data, location data, 3D data, sound data, light data, distance measurements, surveyor data (e.g., cartographic information, etc.), user feedback, user input, temperature data, motion data, and/or the like from user devices 360.

Deployed devices 370 may be devices deployed within a space such as a building. In various embodiments, deployed devices 370 include device associated with a building management system. For example, deployed devices 370 may include dampers, chillers, door locks, AHUs, smart lights, controllers, and/or the like. In various embodiments, deployed devices 370 include specific sensors such as temperature sensors, occupancy sensors, humidity sensors, vibration sensors, glass-break sensors, weight sensors, flow sensors, IR sensors, cameras, and/or the like. In various embodiments, smart configuration and commissioning system 300 receives information from deployed devices 370 and integrates the information into a digital representation of a space such as a building. For example, smart configuration and commissioning system 300 may receive a scan from a building management system that may attempt to identify deployed devices 370 that are controlled by the building management system and may generate digital representations for the identified deployed devices 370. Additionally or alternatively, smart configuration and commissioning system 300 may turn on a smart light that has not been mapped to a space within a building, may receive an indication from a camera that a light turned on in a specific room, may determine that the smart light is located in the specific room, and may update a digital representation of the building to map the smart light to the specific room.

Smart configuration and commissioning system 300 is shown to include processing circuit 310, entity graph database 312, and storage 314. Processing circuit 310 may include processor 316 and memory 318. Memory 318 may have instructions stored thereon that, when executed by processor 316, cause processing circuit 310 to perform the various operations described herein. The operations described herein may be implemented using software, hardware, or a combination thereof. Processor 316 may include a microprocessor, ASIC, FPGA, etc., or combinations thereof. In many embodiments, processor 316 may be a multi-core processor or an array of processors. Memory 318 may include, but is not limited to, electronic, optical, magnetic, or any other storage devices capable of providing processor 316 with program instructions. Memory 318 may include a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, EEPROM, EPROM, flash memory, optical media, or any other suitable memory from which processor 316 can read instructions. The instructions may include code from any suitable computer programming language such as, but not limited to, C, C++, C#, Java, JavaScript, Perl, HTML, XML, Python and Visual Basic. While the present features are described as being implemented using a processor and memory, it should be understood that, in various embodiments, the features could be implemented using any other type of computing or processing architecture, such as special-purpose integrated circuits. Further, in some implementations, processor 316 may include a system of multiple processors and/or memory 318 may include a system of multiple computer-readable storage media. For example, in some implementations, system 300 may be implemented using a distributed computing environment in which multiple computing systems work in a distributed or coordinating fashion to implement various functions described herein. In some such implementations, system 300 may be a cloud computing system in which computing cloud-based resources perform the functions. In various implementations, the functions can be performed by servers, client devices, or a combination thereof.

Memory 318 may include data ingestion circuit 320, data preprocessing circuit 322, semantic extraction circuit 324, dynamic configuration circuit 326, model integration circuit 328, model enrichment circuit 330, and model analysis circuit 332. Data ingestion circuit 320 may be configured to receive data from various sources (e.g., building management system 102, external systems 350, user devices 360, etc.) and ingest the data into a digital representation of a space. For example, data ingestion circuit 320 may receive a csv file having a listing of building assets and may generate object entities for entity graph 170 based on each of the building assets. As another example, data ingestion circuit 320 may receive images of an interior of a space from user devices 360 (e.g., smartphones, etc.) and may embed the images into a 3D model of the space. In various embodiments, data ingestion circuit 320 converts various input formats. For example, data ingestion circuit 320 may convert from serialized BIM to Brick RDF (e.g., a JSON-LD) model, etc.), from Revit to Forge, from Revit BIM to SVF, from COBie to Brick RDF, from SCT to Brick RDF, from RAC to Brick, from Excel to Brick, from an old version of a file format to a new version of a file format, from Revit (e.g., IFC, etc.) to GeoJson, from Revit to TTL, and/or the like. In various embodiments, data ingestion circuit 320 may generate digital representations of buildings, floors, rooms, spaces, zones (e.g., HVAC zones, etc.), and/or the like based on received data. Additionally or alternatively, data ingestion circuit 320 may generate digital representations of relationships between entities (e.g., buildings, floors, etc.) such as spatial relationships (e.g., buildings [hasLocation] Floor [hasLocation] Room [hasLocation] Equipment, Zone (HVAC) [hasPart] Space, etc.). In various embodiments, data ingestion circuit 320 may store point values (e.g., sensor readings, measurements, etc.) based on received data. For example, data ingestion circuit 320 may receive an excel file including temperature readings associated with a space over a period of time and may integrate the temperature readings into a digital representation of the space such as entity graph 170.

Data preprocessing circuit 322 may perform various functions of received data to facilitate ingesting the data into a digital representation. For example, data preprocessing circuit 322 may perform semantic extraction (e.g., via semantic extraction circuit 324, etc.) on input strings to identify descriptive information within the strings (e.g., building identifiers, zone identifiers, equipment identifiers, times, dates, locations, etc.). As another example, data preprocessing circuit 322 may perform various dataset manipulations such as transposing a matrix (e.g., to facilitate simplified data ingestion, etc.) and/or processing data such as determining whether a location of “Madison” associated with a building refers to Madison, WI or Madison, AL.

Semantic extraction circuit 324 may receive data and automatically (e.g., with little to no user intervention, etc.) extract semantic information from the data such as asset identifiers. For example, semantic extraction circuit 324 may receive a string and may analyze the string (e.g., parse the string) using machine learning (or artificial intelligence models such as a neural network, etc.) to extract one or more identifiers embedded within the string. For example, semantic extraction circuit 324 may receive the string “ZONE.AHU01.RM2008S.ZONE_AIR_TEMP” and may identify that the string relates to point data associated with an air temperature in a HVAC zone associated with room 2008S that is served by AHU01. As an additional example, semantic extraction circuit 324 may receive a string and may identify a space, a zone, a point type, and/or the like embedded in the string. In some embodiments, semantic extraction circuit 324 may extract classes and/or identifiers from strings. Semantic extraction from strings is discussed in detail in U.S. patent application Ser. No. 16/885,959 titled, “BUILDING SYSTEM WITH STRING MAPPING BASED ON A STATISTICAL MODEL” filed on May 28, 2020 and U.S. patent application Ser. No. 16/885,968 titled, “BUILDING SYSTEM WITH STRING MAPPING BASED ON A SEQUENCE TO SEQUENCE NEURAL NETWORK” filed on May 28, 2020, the entirety of both of which are incorporated by reference herein. In various embodiments, semantic extraction circuit 324 may extract semantic information from various sources. For example, semantic extraction circuit 324 may extract room names and/or dimensions from a SVG floorplan. In some embodiments, semantic extraction circuit 324 implements a hidden Markov Model (HMM). Additionally or alternatively, semantic extraction circuit 324 may implement a conditional random field (CRF) model. In various embodiments, semantic extraction circuit 324 generates recommendations for a user. For example, semantic extraction circuit 324 may generate semantic recommendations based on analyzing an input string. As another example, semantic extraction circuit 324 may receive the string “SDH_CHW2_CHP4_ALM” and may generate a recommendation that “SDH” is a building and “CHW” is either chilled water or cold water. In various embodiments, semantic extraction circuit 324 trains one or more models using training data. For example, semantic extraction circuit 324 may be trained on prelabeled strings (e.g., strings that a user has performed semantic extraction on, etc.). As an additional example, semantic extraction circuit 324 may be trained on entity names (e.g., point names, etc.). In various embodiments, semantic extraction circuit 324 augments one or more systems, such as OpenRefine (e.g., through one or more plugins or operators, etc.).

Dynamic configuration circuit 326 may modify operation of one or more devices and derive information therefrom. For example, dynamic configuration circuit 326 may turn on a fan associated with a known location within a building, may monitor one or more temperature sensors that have not been mapped to a location within the building, and may determine based on a change in temperature associated with one or more of the sensors that the one or more sensors are located in the location. In various embodiments, dynamic configuration circuit 326 is configured to fill gaps in a digital representation of a space. For example, a digital representation such as entity graph 170 may include a number of unlabeled/unassigned assets (e.g., smart lights not yet associated with a room, etc.) and dynamic configuration circuit 326 may derive the missing labels/assignments by operating various assets within a building (e.g., as described above, etc.). In various embodiments, dynamic configuration circuit 326 generates and/or modifies relationships within entity graph 170.

Model integration circuit 328 may integrate one or more models into a digital representation of a space. For example, model integration circuit 328 may receive a BIM associated with a building and may integrate information included in the BIM into a digital representation of the building (e.g., entity graph 170, etc.). In various embodiments, model integration circuit 328 extracts semantic information from data. For example, model integration circuit 328 may receive a simple vector format (SVF) file depicting a floorplan of a building and may extract dimensions of rooms in the floorplan from the SVF file and embed the dimensions in a digital representation of the building (e.g., entity graph 170, etc.). In various embodiments, model integration circuit 328 deduplicates between identifiers in various models. For example, a first model may include building data having an asset labeled with a first identifier and a second model may include building data having the asset labeled with a second identifier and model integration circuit 328 may determine that the first and second identifiers are associated with the same asset and may only generate a single instance of the first asset in a digital twin. In various embodiments, model integration circuit 328 uses machine learning and/or artificial intelligence to deduplicate between entities (e.g., asset identifiers, etc.).

In various embodiments, model integration circuit 328 may modify/enrich an existing model, such as a BIM, to become a digital twin of a building (e.g., use the BIM as entity graph 170 or change the BIM into entity graph 170, etc.). In various embodiments, model integration circuit 328 modifies existing models to include new information. For example, entity graph 170 may include more complete information than an imported BIM and model integration circuit 328 may modify the imported BIM to include the additional information included in entity graph 170. In various embodiments, model integration circuit 328 selects between versions of models. For example, model integration circuit 328 may prefer “central” BIM models if available but may also use “local” BIM models. In various embodiments, model integration circuit 328 resolves conflicts between various versions of models. For example, model integration circuit 328 may analyze two conflicting model versions to determine one or more aspects from each of the conflicting models that is most accurate and may ingest the one or more aspects.

In some embodiments, model integration circuit 328 augments one or more existing models. For example, model integration circuit 328 may link a COBie model with a graphical model using a unique identifier (e.g., a key, etc.). As another example, model integration circuit 328 may embed spatial metadata (e.g., floorplan dimensions, etc.) into a SVG floorplan. As yet another example, model integration circuit 328 may generate custom data within a model such as a BIM. For example, model integration circuit 328 may generate custom data (e.g., key/value pairs, etc.) and embed the custom data into a model such as a BIM to augment the model with more granular of data than would be natively possible. In various embodiments, model integration circuit 328 facilitates transferring augmentations between versions of models. For example, model integration circuit 328 may augment a first version of a BIM and may automatically augment (e.g., sync, etc.) a second version of the BIM when it is released. In various embodiments, model integration circuit 328 maintains a unique identifier for each model version. Additionally or alternatively, model integration circuit 328 may maintain unique identifiers for objects within a model. For example, model integration circuit 328 may generate a stable identifier for objects within a BIM model. As an additional example, model integration circuit 328 may generate global identifiers for objects within a model such as a BIM model and may automatically reapply the global identifiers to a subsequent version of the model. Global identifiers may facilitate performing operations across models (e.g., searching for an entity in entity graph 170 and/or a BIM model, etc.). In various embodiments, global identifiers are stored in a graph data structure (e.g., a digital twin of a space, entity graph 170, etc.). Therefore, a model such as a BIM model or a digital twin may display visual data such as a 3D model by retrieving a SVF model using the global identifier. In various embodiments, this solves existing problems associated with visualizing a space (e.g., in a web browser, etc.). In some embodiments, model integration circuit 328 facilitates serializing models. For example, model integration circuit 328 may serialize a BIM model.

Model enrichment circuit 330 may enrich an existing model using derived information. For example, model enrichment circuit 330 may enrich a digital representation of a building (e.g., entity graph 170, etc.) with information derived from the digital representation. In some embodiments, model enrichment circuit 330 generates a health metric associated with data. For example, model enrichment circuit 330 may operate an HVAC unit associated with a space, monitor the temperature in the space with a number of temperature sensors, and based on determining that one of the number of temperature sensors deviated from the rest of the number of temperature sensors may determine that either (i) the one temperature sensor is malfunctioning (e.g., needs to be repaired, etc.) or (ii) the one temperature sensor is not actually located in the space. To continue the previous example, based on the determination, model enrichment circuit 330 may modify point values (e.g., sensor readings, etc.) associated with the one temperature sensor to include a health metric that indicates that the “health” of the data (e.g., the validity, the trustworthiness, etc.) is in question. In various embodiments, systems (e.g., building management system 102, etc.) may use such health metrics during analysis and operation (e.g., discount sensor readings from sensors having health metrics that indicate their data may be untrustworthy, etc.). In various embodiments, model enrichment circuit 330 may derive information from existing data such as determining floorplan dimensions from a SVG floorplan.

In some embodiments, model enrichment circuit 330 enriches a digital representation of a space (e.g., a graph data structure, entity graph 170, etc.) with visualization data. For example, model enrichment circuit 330 may link 2D and/or 3D data to entities within a graph data structure to facilitate bidirectional control of data. For example, a user viewing a model of a building may select an element in the model to retrieve attribute information stored in a graph data structure representing the building. As another example, a user viewing a graph data structure of a building may select one of the entities in the graph data structure to retrieve a visual representation of the entity (e.g., a 3D model, etc.). As yet another example, a user viewing a 3D model of a building may wish to know which cameras have a view of a particular doorway and may select the doorway to retrieve information from a graph data structure storing a cameras and their associated views in order to retrieve links to camera feeds associated with cameras that have a view of the doorway.

Model analysis circuit 332 may analyze one or more models to generate information and/or actions. For example, model analysis circuit 332 may analyze a digital representation of a space to generate information such as predicted maintenance for assets within the space. In various embodiments, model analysis circuit 332 may analyze the relationships between points (e.g., data entities, etc.) to determine if behavior of one or more entities (e.g., a HVAC unit, etc.) is anomalous. For example, model analysis circuit 332 may perform pattern matching, performance modeling, and/or the like. In some embodiments, model analysis circuit 332 configures one or more other systems based on one or more models. For example, model analysis circuit 332 may generate fault detection and diagnostic (FDD) thresholds for a FDD system based on a digital twin of a space. In some embodiments, model analysis circuit 332 compares between models to generate information. For example, model analysis circuit 332 may compare a first digital twin associated with a first point in time to a second digital twin associated with a second point in time to identify changes and/or update configurations between models.

Entity graph database 312 may store one or more digital representations of spaces, people, and/or assets. In some embodiments, entity graph database 312 stores a digital representation of a building such as a digital twin. In various embodiments, entity graph database 312 includes one or more graph data structures such as entity graph 170. In various embodiments, smart configuration and commissioning system 300 updates entity graph database 312 to include information from building management system 102, cloud building management platform 140, external systems 350, user devices 360, deployed devices 370, and/or the like. In various embodiments, the one or more digital representations of spaces, people, and/or assets may represent the past and present states of the spaces, people, and/or assets and may be used to predict future states of the spaces, people, and/or assets. For example, each graph data structure (e.g., digital representation, etc.) may be serialized such that comparisons between versions of a digital twin may reveal how an entity associated with the digital twin changed over time. In some embodiments, changes in serialized versions of a graph data structure (or usage thereof) may trigger one or more actions. For example, if a new set of sensors is added to a data structure representing a room then smart configuration and commissioning system 300 may trigger a review of data associated with the room to determine things like if the room was split in two (e.g., during a renovation, etc.), and/or to determine whether a control scheme associated with the room needs to be updated. As an additional example, smart configuration and commissioning system 300 may determine that an occupancy sensor is no longer activated in a room and may trigger a review to determine if a change such as a sensor error or the room being converted into a storage closet are to blame. In some embodiments, actions are triggered across models. For example, if a new space is added to a security system then smart configuration and commissioning system 300 may review a lighting system to see if the change is reflected in the lighting system. As an additional example, if a space is added to a lighting controls system then smart configuration and commissioning system 300 may analyze an access control system to determine if the access policies for doors associated with the space can be inherited from the lighting controls system. In some embodiments, serialized digital twins are stored as a csv file, however one of skill in the art will appreciate that any suitable format may be used (e.g., RDF TTL, etc.). In various embodiments, data structures in entity graph database 312 represent parent/child relationships. Additionally or alternatively, entity graph database 312 may include information such as a space type, space dimensions, geocoordinates (e.g., associated with an entity such as a person or asset, etc.), a location of a nearest exit, locations of amenities (e.g., restrooms, kitchens, etc.), a link to a photograph of the space, a link to a floorplan file, and/or the like.

Entity graph database 312 may include one or more storage mediums. The storage mediums may include but are not limited to magnetic storage, optical storage, flash storage, and/or RAM. Entity graph database 312 may implement or facilitate various APIs to perform database functions (i.e., managing data stored in entity graph database 312). The APIs can be but are not limited to SQL, ODBC, JDBC, and/or any other data storage and manipulation API.

Storage 314 may store information associated with the operation of smart configuration and commissioning system 300. For example, storage 314 may store one or more machine learning models configured to analyze building data and generate predicted future states of operation. One of skill in the art will understand that storage 314 may suitable store many types of data and that, for the sake of brevity, all such types of data are not enumerated herein. Storage 314 may include one or more storage mediums. The storage mediums may include but are not limited to magnetic storage, optical storage, flash storage, and/or RAM. Storage 314 may implement or facilitate various APIs to perform database functions (i.e., managing data stored in storage 314). The APIs can be but are not limited to SQL, ODBC, JDBC, and/or any other data storage and manipulation API.

Referring now to FIG. 4, method 400 is shown for generating an entity graph, according to an exemplary embodiment. In various embodiments, smart configuration and commissioning system 300 performs method 400. For example, smart configuration and commissioning system 300 may receive information associated with a building being converted to use a building management system that utilizes a digital representation of the building and may generate a digital representation of the building (e.g., entity graph 170, etc.). At step 402, smart configuration and commissioning system 300 may receive space data. Space data may include data describing a layout of a space. In some embodiments, the space data includes hierarchies. For example, the space data may describe a building having floors which have rooms, etc. The space data may take many forms. For example, the space data may include a space graph (e.g., a graph data structure describing a space, etc.), a BIM, a floorplan, a list of spaces (e.g., for a custom dictionary, etc.), a user description, and/or the like. Additionally or alternatively, the space data may include data generated based on a scan of the building. For example, a building management system may perform a discovery operation (e.g., a scan, etc.) to discover connected devices within a building and may pass the discovered information to smart configuration and commissioning system 300. Additionally or alternatively, smart configuration and commissioning system 300 may receive other data (e.g., asset data, HR data, etc.). For example, in some embodiments, the space data may include a points list indicating data points associated with spaces.

At step 404, smart configuration and commissioning system 300 may generate object entities in an entity graph associated with spaces. Object entities associated with spaces can also be referred to as digital twins of the spaces, or space digital twins. For example, smart configuration and commissioning system 300 may generate object entities for each space included in the space data. For example, smart configuration and commissioning system 300 may generate a node corresponding to a building and a node corresponding to floor within the building and a node corresponding to a room within the floor. In various embodiments, smart configuration and commissioning system 300 may generate spatial relationships and/or classify spaces. For example, smart configuration and commissioning system 300 may generate an edge between a node representing a building and a node representing a floor within the building, wherein the edge represents a relationship between the building and the floor (e.g., building [hasFloor] floor, etc.).

At step 406, smart configuration and commissioning system 300 may generate object entities in an entity graph associated with assets. For example, smart configuration and commissioning system 300 may generate object entities for each asset included in a building. Object entities associated with assets may also be referred to as digital twins of the assets, or asset digital twins. For example, smart configuration and commissioning system 300 may generate a node corresponding to a HVAC system and a node corresponding to a fan within the HVAC system. In various embodiments, smart configuration and commissioning system 300 may generate asset relationships and/or classify assets (e.g., asset types, etc.). For example, smart configuration and commissioning system 300 may generate an edge between a node representing a HVAC system and a node representing a fan within the HVAC system, wherein the edge represents a relationship between the HVAC system and the fan (e.g., HVAC system [hasComponent] fan, etc.). Assets may include people, devices, furniture, lights, objects, supplies, equipment (e.g., HVAC equipment, etc.), decorations, and/or the like. Smart configuration and commissioning system 300 may receive asset information from an EAM system, a COBie file, a list of assets (e.g., for a custom dictionary, etc.), and/or the like. In some embodiments, the asset entities may be generated using data from a points list, such as data that may not necessarily be explicitly tied to an asset but may be indicative of the presence of an asset in a space. In various embodiments, smart configuration and commissioning system 300 may generate an asset graph (e.g., a graph data structure representing assets, etc.).

At step 408, smart configuration and commissioning system 300 may assign assets to spaces. For example, smart configuration and commissioning system 300 may generate relationships between object entities representing spaces and object entities representing assets. For example, smart configuration and commissioning system 300 may generate an edge between a node representing a HVAC system and a node representing a building, wherein the edge represents a relationship between the HVAC system and the building (e.g., HVAC system [servesSpace] building, etc.). In various embodiments, the relationships are bidirectional relationships. For example, an edge may represent a first relationships (e.g., FAN_1 [isControlledBy] CONTROLLER_2, etc.) and a second relationship (e.g., CONTROLLER_2 [Controls] FAN_1, etc.). In some implementations, the bidirectional relationships may not necessarily be direct counterparts or reciprocal relationships; for example, an edge between a thermostat entity and a room entity may indicate that the thermostat is in the room, and another edge may indicate that the room temperature is controlled by the thermostat. In some embodiments, smart configuration and commissioning system 300 assigns assets to spaces using relationships from received data (e.g., existing relationships, etc.). For example, smart configuration and commissioning system 300 may determine relationships between assets and spaces using information from an existing graph data structure (e.g., from a COBie file, from a EAM system, etc.), from a manual site survey, a list of assigned assets (e.g., for a custom dictionary, etc.), by processing strings to extract semantic information (e.g., as described above, etc.), via data manipulation (e.g., matching and/or clustering data, etc.), and/or the like.

At step 410, smart configuration and commissioning system 300 may assign data entities, or points, to assets. For example, smart configuration and commissioning system 300 may generate a data entity representing a point, or measurement value, and link (e.g., via an edge, etc.) the data entity to an asset (e.g., a node, etc.). In various embodiments, data entities include point values (e.g., sensor measurements, etc.). In various embodiments, smart configuration and commissioning system 300 may determine relationships between point values and assets. For example, smart configuration and commissioning system 300 may receive BMS configuration files and use the configuration files to link point values to assets (e.g., TEMP_SENSOR_1 has measurement “70_deg_F” at “14:20:02” on “10/2/2020,” etc.). Step 410 may include generating data entities (e.g., generating data entities from received sensor information, etc.). In some embodiments, smart configuration and commissioning system 300 assigns data entities to assets using relationships from received data (e.g., existing relationships, etc.). For example, smart configuration and commissioning system 300 may determine relationships between data entities and assets using information from an external point schedule, a site survey, by processing strings to extract semantic information (e.g., as described above, etc.), and/or the like. In various embodiments, step 410 includes assigning asset-less data entities to spaces. For example, smart configuration and commissioning system 300 may receive a list of sensor readings some of which are not associated with specific sensors and may assign the sensor readings that are not associated with specific sensors to a space (e.g., the space from which the sensors readings originated, etc.). In some embodiments, the points/measured values may be ingested into the entities for the assets/spaces themselves, as opposed to being represented as separate entities/nodes in the graph.

At step 412, smart configuration and commissioning system 300 may classify data entities. For example, smart configuration and commissioning system 300 may generate a relational object (e.g., an edge, etc.) between the data entity and a class entity. For example, smart configuration and commissioning system 300 may generate an edge representing that a sensor reading such as “70_deg_F” is of type TEMP. In some embodiments, smart configuration and commissioning system 300 classifies data entities using relationships from received data (e.g., existing classifications, etc.). For example, smart configuration and commissioning system 300 may classify data entities using information from built-in tags (e.g., of received data, etc.), a standard naming scheme, an existing schedule, “typical” naming (e.g., industry standards, etc.), historical databases (e.g., using an AI classifier, etc.), and/or the like. In some embodiments, step 412 is performed at least partially manually.

At step 414, smart configuration and commissioning system 300 may generate relational objects. For example, smart configuration and commissioning system 300 may generate edges in a graph data structure (e.g., entity graph 170, etc.) that represent relationships between nodes in the graph data structure. The relationships may include relationships between points (e.g., data entities, etc.), assets (e.g., object entities, etc.), and spaces (e.g., object entities, etc.). For example, the relationships may include an edge representing that a AHU serves a room (e.g., AHU_1 [servesSpace] ROOM_20S, etc.), an edge representing that a chiller feeds a bathroom (e.g., CHILLER_20 [Feeds] BATHROOM_20LL, etc.), an edge representing that an occupancy sensor measures the occupancy of a room (e.g., SENSOR_S98 [Measures] ROOM_22S, etc.). In some embodiments, smart configuration and commissioning system 300 relational objects from received data (e.g., existing relationships, etc.). For example, smart configuration and commissioning system 300 may generate relationships using information from built-in “slots” within tags (e.g., of received data, etc.), a standard naming scheme, an existing schedule, “typical” naming (e.g., industry standards, etc.), historical databases (e.g., using an AI classifier, etc.), and/or the like. In some embodiments, step 414 is performed at least partially manually. At step 416, smart configuration and commissioning system 300 may export an entity graph. For example, smart configuration and commissioning system 300 may export an entity graph constructed during method 400 for use by other systems (e.g., a BMS, etc.).

Referring now to FIG. 5, method 500 for classifying input strings is shown, according to an exemplary embodiment. In various embodiments, smart configuration and commissioning system 300 performs method 500. For example, smart configuration and commissioning system 300 may classify input strings using statistical analysis and/or machine learning as described above with reference to FIG. 3. In various embodiments, method 500 include the use of one or more APIs and/or SDKs to facilitate ingesting data from external sources. For example, a technician may use an API to ingest data and extract semantic information from an existing BMS into a digital representation associated with a newly installed system. In some embodiments, method 500 is performed automatically (e.g., with little to no user intervention, etc.). Additionally or alternatively, method 500 may be performed at least partially manually by a user (e.g., during a setup phase of a system installation, etc.). At step 502, smart configuration and commissioning system 300 may receive unclassified data. Unclassified data may include raw metadata, strings, and/or the like. For example, smart configuration and commissioning system 300 may receive strings from BMS configuration files (e.g., of a BMS system that is being upgraded, etc.) that are associated with a scan (e.g., a BACnet point scan, etc.) to discover connected devices within a building. In various embodiments, the unclassified data includes semantic information to be extracted.

At step 504, smart configuration and commissioning system 300 may identify semantic elements within the unclassified data. For example, step 504 may include parsing a string using a separator (e.g., separating “ZONE.AHU01.RM2008S.ZONE_AIR_TEMP” into “ZONE,” “AHU01,” “RM2008S,” “ZONE_AIR_TEMP,” etc.). In various embodiments, the semantic elements include object entities, data entities, and/or relationships between various entities. For example, smart configuration and commissioning system 300 may extract the relationship FLOOR_2 [hasLocation] BUILDING_1 from a string. In some embodiments, step 504 includes executing one or more machine learning models. For example, step 504 may include executing a LSTM model with character embedding to extract semantic information from strings. In some embodiments, step 504 includes surfacing proposed semantic elements (e.g., generated based on ML analysis, etc.) to a user for selection. In some embodiments, method 500 includes feeding a ML and/or AI model one or more examples of user-classifications (e.g., a string a user has manually classified, etc.) to train the ML and/or AI model and facilitate automated extraction of the rest of the strings.

At step 506, smart configuration and commissioning system 300 may segregate unclassified data based on the semantic elements. For example, smart configuration and commissioning system 300 may generate a multi-dimensional matrix having columns associated with the identified semantic elements from a 1-dimensional list of strings. In some embodiments, steps 504 and 506 repeat until a number (e.g., a threshold amount, etc.) of possible semantic elements have been extracted.

At step 508, smart configuration and commissioning system 300 may identify related elements within unclassified data. For example, smart configuration and commissioning system 300 may identify a number of sensors that are all of type TEMP_SENSOR. As another example, smart configuration and commissioning system 300 may identify a number of rooms that are on a specific floor of a building. In various embodiments, step 508 includes executing one or more machine learning models. For example, step 506 may include using a machine learning classifier to identify related elements within the data. Additionally or alternatively, one or more algorithms may be used to identify related elements. For example, an n-gram algorithm may be used to identify related elements.

At step 510, smart configuration and commissioning system 300 may cluster unclassified data based on related elements. For example, smart configuration and commissioning system 300 may cluster specific rows of a multi-dimensional matrix associated with elements that are related. In some embodiments, step 510 includes assigning one or more elements to a group. For example, a number of AHUs that serve a floor within a building may be tagged as serving the floor. In some embodiments, steps 1109 and 510 repeat until a number (e.g., a threshold amount, etc.) of possible related elements have been clustered.

At step 512, smart configuration and commissioning system 300 may label entities. For example, smart configuration and commissioning system 300 may label entities in a matrix as data entities, object entities, class entities, and/or the like. In various embodiments, smart configuration and commissioning system 300 may generate labeled entities (e.g., for an entity graph, etc.) based on the information surfaced in previous steps. For example, smart configuration and commissioning system 300 may label a row as being associated with a temperature sensor. In various embodiments, data in the matrix is grouped by entity. For example, entries in a first column of a matrix may be associated with unique entities (e.g., people, places, and/or assets, etc.).

At step 514, smart configuration and commissioning system 300 may transpose the matrix. In various embodiments, step 514 is optional. For example, step 514 may facilitate converting a matrix representing attributes associated with a number of entities into a matrix representing timeseries values generated by a number of entities. At step 516, smart configuration and commissioning system 300 may reconcile the matrix elements with an external system. In various embodiments, the external system is a server configured to receive data and perform one or more operations on the data. Additionally or alternatively, the external server may be part of smart configuration and commissioning system 300. For example, step 516 may include sending location information associated with one or more entities to a server to determine whether the location information is associated with a first location or a second location (e.g., whether “Madison” refers to Madison, WI or Madison, AL, etc.). In various embodiments, reconciling the matrix may include computing one or more functions using the matrix information. For example, smart configuration and commissioning system 300 may enrich the matrix information by reconciling the matrix to derive distance measurements associated with the distance between each entity within the matrix (e.g., the distance between a first building and a second building, etc.). At step 518, smart configuration and commissioning system 300 may export the matrix. In various embodiments, the matrix may be used by other systems to determine information and/or actions. For example, data ingestion circuit 320 may use the matrix to generate a digital representation of a space and/or model integration circuit 328 may use the matrix to integrate existing data (e.g., extracted from unclassified strings provided by a legacy BMS, etc.) into a digital representation.

Referring now to FIGS. 6A-B, diagram 600 illustrating input string manipulation is shown, according to an exemplary embodiment. In various embodiments, smart configuration and commissioning system 300 implements input string manipulation as shown in diagram 600. In various embodiments, diagram 600 illustrates input string manipulation as described in detail above with reference to FIG. 5. Diagram 600 starts with input list 610. Input list 610 may include a number of unclassified strings. For example, input list 610 may include a number of strings from a BMS configuration file. As another example, input list 610 may include a number of strings from a BACnet point scan (e.g., a BMS discovery scan, etc.). Input list 610 may include one or more input strings 612. Input strings 612 may be associated with an entity (e.g., people, places, and/or assets, etc.) within a space such as a building. Additionally or alternatively, input strings 612 may be associated with data (e.g., a data entity, a point value, etc.). In various embodiments, input strings 612 include semantic information (e.g., information describing a space and/or entities within a space, etc.).

At step 602, smart configuration and commissioning system 300 may segregate input list 610 based on semantic elements to generate matrix 620. For example, smart configuration and commissioning system 300 may generate one or more columns based on input list 610. Matrix 620 may include elements 622-628. One of skill in the art will appreciate that matrix 620 may include any number of elements (e.g., five elements, a thousand elements, etc.). Each of elements 622-628 may be associated with different information. For example, a first element 622 may be associated with a space type (e.g., ZONE, etc.), a second element 624 may be associated with a device identifier (e.g., AH1N, etc.), a third element 626 may be associated with a room identifier (e.g., RM159, etc.), and a fourth element 628 may be associated with a data type (e.g., Day/Night, etc.).

At step 604, smart configuration and commissioning system 300 may cluster data based on related elements. For example, smart configuration and commissioning system 300 may cluster matrix 620 based on similar values (e.g., “heat cool” vs. “heat coool,” etc.). In various embodiments, step 604 may facilitate correcting errors (e.g., user input errors, etc.). In some embodiments, step 604 includes performing one or more matrix operations on matrix 620. For example, smart configuration and commissioning system 300 may transpose matrix 620 to generate transposed matrix 630. Transposed matrix 630 may include a number of parameters 634-644 associated with a specific entity (e.g., entity 632, “ZONE:AH1N:RM157,” etc.). Parameters 634-644 may include one or more variable values and/or pointers to one or more variable values. For example, parameters 634-644 may include a zone air temperature (e.g., Zone Air Temp_1397, etc.), a day/night parameter (e.g., Day Night_5874, etc.), a heat/cool parameter (e.g., Heat Cool_7354, etc.), an auxiliary temperature (e.g., AUX TEMP_1009, etc.), a control setpoint (e.g., CTL STPT_4703, etc.), a valve command (e.g., VLV 1 COMD_4223, etc.), and/or the like. In various embodiments, transposed matrix 630 may be used by smart configuration and commissioning system 300 to setup and/or modify a digital representation of a space such as a graph data structure.

Referring now to FIG. 7, system 700 for auto-commissioning of a space, shown as room 702, is shown, according to an exemplary embodiment. In various embodiments, smart configuration and commissioning system 300 auto-commissions room 702. For example, smart configuration and commissioning system 300 may analyze a graph data structure representing room 702, identify information in the graph data structure that needs completing/updating, and perform one or more operations to derive information to at least partially complete and/or update the graph data structure. As a general example, smart configuration and commissioning system 300 may receive an entity graph (e.g., entity graph 170, etc.) with a number of unlabeled sensors (e.g., sensors not associated with a specific space, etc.) and may operate one or more HVAC systems within a building while measuring operation of the number of unlabeled sensors to determine a location within the building that each of the number of unlabeled sensors is associated with. Room 702 is shown to include thermostat 704 and temperature sensor 706. In some embodiments, room 702 includes one or more users. In some embodiments, the one or more users having devices (e.g., mobile devices, etc.) that may be used to provide feedback to smart configuration and commissioning system 300.

At step 710, smart configuration and commissioning system 300 may modify an operation of room 702 and/or one or more entities (e.g., building assets, etc.) associated with room 702. For example, smart configuration and commissioning system 300 may modify operation of an HVAC system serving room 702, may modify operation of one or more smart lights located in room 702, and/or the like. In various embodiments, step 710 modifies one or more environmental parameters associated with room 702. For example, step 710 may modify a temperature of room 702, a lighting in room 702, a sound level in room 702, an asset within room 702 (e.g., turning on/off a television, etc.), and/or the like.

At step 720, smart configuration and commissioning system 300 may measure an effect of step 710. Additionally or alternatively, smart configuration and commissioning system 300 may receive measurements from another system (e.g., building management system 102, etc.). Step 720 may include receiving sensor information from one or more sensors. For example, step 720 may include receiving a temperature measurement from at least one of thermostat 704 and/or temperature sensor 706.

At step 730, smart configuration and commissioning system 300 may modify a graph data structure such as entity graph 170. For example, smart configuration and commissioning system 300 may modify entity graph 170 to generate an edge connecting a node representing thermostat 704 to a node representing room 702, wherein the edge represents that thermostat 704 is located within room 702. In various embodiments, step 730 includes analyzing received data to determine relationships between various entities. For example, smart configuration and commissioning system 300 may determine that thermostat 704 is located in room 702 because thermostat 704 measured a temperature increase corresponding to what would be expected based on operation of a HVAC system during step 710. In some embodiments, step 730 includes generating data to fill in gaps in an existing graph data structure (e.g., a digital representation of room 702, etc.). For example, smart configuration and commissioning system 300 may generate relationship data describing a location of a sensor for an existing sensor that is missing a location. In various embodiments, step 730 includes analyzing data from various sources. For example, step 730 may include analyzing user feedback from one or more users located in a room. For example, smart configuration and commissioning system 300 may operate a HVAC system that is not linked to which rooms it serves and may receive user feedback indicating that a specific room suddenly changed temperature (or became uncomfortable, etc.) and may determine that the HVAC system serves the specific room. As another example, smart configuration and commissioning system 300 may turn on/off a television that has not been associated with a specific space in a digital representation of a building and may receive user feedback that a television in a specific room turned on/off and may determine that the television is located in the specific room. In various embodiments, step 730 includes generating one or more nodes and/or one or more edges.

Additionally or alternatively, step 730 may include generating one or more health metrics. For example, smart configuration and commissioning system 300 may generate a health metric associated with thermostat 704 that indicates that data from thermostat 704 may not be valid. As a brief example, smart configuration and commissioning system 300 may determine that thermostat 704 is supposedly located in room 702, however upon changing a temperature of room 702, smart configuration and commissioning system 300 may only observe a change in temperature measurements from temperature sensor 706 (e.g., and/or one or more other temperature sensors, etc.) and may determine that thermostat 704 may be malfunctioning (e.g., not producing reliable data) and may generate a health metric associated with thermostat 704. Additionally or alternatively, smart configuration and commissioning system 300 may determine that thermostat 704 is in fact not located in room 702 (e.g., which would explain why thermostat 704 did not register a change in temperature, etc.). In various embodiments, health metrics may be used to reduce nuisance alarms and/or prevent alarm fatigue. For example, building management system 102 may suppress alarms associated with data having a health metric that indicates that the data may be unreliable.

Referring now to FIG. 8, system 800 for digesting information into an entity graph is shown, according to an exemplary embodiment. In various embodiments, smart configuration and commissioning system 300 digests information into a graph data structure such as entity graph 170. In various embodiments, smart configuration and commissioning system 300 may generate a digital representation of a space based on ingested data. System 800 is shown to include entity graph database 312. In various embodiments, entity graph database 312 includes one or more graph data structures. Smart configuration and commissioning system 300 may receive data from one or more sources and integrate the received data into one or more graph data structures of entity graph database 312. In various embodiments, by combining a number of data sources smart configuration and commissioning system 300 may generate a more complete and detailed representation of an entity. In various embodiments, digesting information into an entity graph may include eliminating duplicates between and/or within datasets. For example, a first HR database may include a first individual and a second visitor registration system may include the first individual and smart configuration and commissioning system 300 may deduplicate the first individual to create a single data representation for the first individual.

In various embodiments, sources 802-832 are integrated (e.g., by smart configuration and commissioning system 300, data ingestion circuit 320, data preprocessing circuit 322, semantic extraction circuit 324, model integration circuit 328, etc.) into entity graph database 312. For example, smart configuration and commissioning system 300 may integrate information from BIM 802 (e.g., IFC files, Revit files, RDF graph models, TTL data, etc.), building floorplan 804 (e.g., SVG images, etc.), HR database 806, email directory 808, ACS 810, EAM system 812 (e.g., COBie files, excel documents, csv files, etc.), facility management systems/software 814, security system 816, indoor maps 818, system scan 820 (e.g., a BACnet scan, etc.), BMS 822, DHCP server logs 824, lighting control system 826, visitor registration system 828, parking management system 830, user device 832, and/or the like.

Referring now to FIG. 9, method 900 of augmenting an entity graph with additional information is shown, according to an exemplary embodiment. In various embodiments, smart configuration and commissioning system 300 performs method 900. For example, smart configuration and commissioning system 300 may perform method 900 to generate additional information for a graph data structure that may facilitate operation of a space such as a building. In various embodiments, the additional information includes a health metric. The health metric may be associated with an accuracy of data. For example, a health metric associated with a relationship between two entities may indicate the likelihood that the relationship is true. As another example, a health metric associated with a sensor reading may indicate the likelihood that the sensor reading is accurate (e.g., describes a true value associated with a measured parameter, etc.).

At step 910, smart configuration and commissioning system 300 may identify one or more entities within an entity graph. For example, smart configuration and commissioning system 300 may identify a node within a graph data structure corresponding to an asset within a building. At step 920, smart configuration and commissioning system 300 may operate the identified entity 920. Additionally or alternatively, step 920 may include operating one or more entities associated with the identified entity. For example, the identified entity may include a room and step 920 may include operating a HVAC system that serves the room (or is thought to serve the room, etc.).

At step 930, smart configuration and commissioning system 300 may compare an effect of operating the entity to an expected effect. For example, smart configuration and commissioning system 300 may expect that operating a HVAC system associated with the room would result in measuring an increased temperature in the room, however smart configuration and commissioning system 300 may observe that no such change occurs. In various embodiments, step 930 includes receiving measurements from one or more entities (e.g., assets, devices, users, etc.). For example, smart configuration and commissioning system 300 may measure the effect of operating an entity (e.g., step 920, etc.).

At step 940, smart configuration and commissioning system 300 may generate a health metric associated with the entity based on the comparison. For example, smart configuration and commissioning system 300 may generate a metric that describes the reliability of sensor data. As another example, smart configuration and commissioning system 300 may generate a metric that describes the likelihood that an edge representing a relationship between two nodes is accurate. In various embodiments, smart configuration and commissioning system 300 may generate a health metric associated with a high degree of accuracy/confidence based on observing an effect of operating the entity consistent with an expected effect. Similarly, smart configuration and commissioning system 300 may generate a health metric associated with a low degree of accuracy/confidence based on observing an effect of operating the entity that deviates (e.g., by some threshold, etc.) from an expected effect. For example, a room may include a number of temperature sensors and in response to operating an HVAC system serving the room all of the number of temperature sensors in the room except for one may indicate a change in temperature. To continue the example, smart configuration and commissioning system 300 may determine that the one temperature sensor is faulty (e.g., producing unreliable temperature readings, etc.) and/or the one temperature sensor is not actually located in the room (e.g., a digital representation of the temperature sensor has been incorrectly mapped to the room, etc.). Based on the determination, smart configuration and commissioning system 300 may perform one or more actions such as saving a health metric associated with point values of the temperature sensor (e.g., the health metric indicating that the point values have a low degree of reliability, etc.), ordering maintenance on the temperature sensor, updating a graph data structure to correctly map the temperature sensor with a new space, and/or the like. At step 950, smart configuration and commission system 300 may store the health metric in the entity graph. For example, smart configuration and commissioning system 300 may generate a new data structure (e.g., a data entity, a node, etc.) associated with point values of the temperature sensor.

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.

In various implementations, the steps and operations described herein may be performed on one processor or in a combination of two or more processors. For example, in some implementations, the various operations could be performed in a central server or set of central servers configured to receive data from one or more devices (e.g., edge computing devices/controllers) and perform the operations. In some implementations, the operations may be performed by one or more local controllers or computing devices (e.g., edge devices), such as controllers dedicated to and/or located within a particular building or portion of a building. In some implementations, the operations may be performed by a combination of one or more central or offsite computing devices/servers and one or more local controllers/computing devices. All such implementations are contemplated within the scope of the present disclosure. Further, unless otherwise indicated, when the present disclosure refers to one or more computer-readable storage media and/or one or more controllers, such computer-readable storage media and/or one or more controllers may be implemented as one or more central servers, one or more local controllers or computing devices (e.g., edge devices), any combination thereof, or any other combination of storage media and/or controllers regardless of the location of such devices.

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

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

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

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

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

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

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

Claims

1. A method for commissioning a building, comprising: identifying a building asset within a digital representation of the building having one or more parameters describing relationships of the building asset within the building;selecting and executing, based on a type of sensor data monitored by the building asset, a predetermined control action for one or more other building assets included within the digital representation to change an environmental parameter of the building;monitoring a response of at least one of the building asset or one of the one or more other building assets to the change in the environmental parameter; andupdating the digital representation to include a parameter defining a health metric for a relationship between the building asset and at least one of the one or more other building assets based on a comparison of the monitored response to the predetermined control action to an expected response, wherein the parameter defining the health metric describes a confidence level in a validity of one of the one or more parameters.
2. The method of claim 1, further comprising updating at least one of the one or more parameters using the parameter defining the health metric.
3. The method of claim 1, wherein updating the digital representation includes analyzing a covariance between sensor measurements of the one or more other building assets associated with the change in the environmental parameter of the building.
4. The method of claim 1, wherein the digital representation includes a graph data structure and wherein the method further comprises updating one or more portions of the graph data structure to represent the relationship of the building asset.
5. The method of claim 1, wherein the digital representation includes a graph data structure and wherein the method further comprises comparing versions of the graph data structure representing at least a portion of the building to identify changes between the versions.
6. The method of claim 5, wherein identifying the building asset includes selecting an asset based on an identified change between the versions of the graph data structure.
7. 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: receive a digital representation of a building having nodes describing entities representing spaces, locations, building assets, points, or events within the building and edges describing one or more relationships between nodes, the digital representation comprising an identified building asset;control, based on a type of sensor data monitored by the identified building asset, one or more first building assets included within the digital representation to change an environmental parameter of the building;monitor a response of the identified building asset, at least one of the one or more first building assets, or one or more second building assets to the change in the environmental parameter; andupdate the digital representation to include a parameter defining a health metric of a node or edge associated with the one or more second building assets or the identified building asset based on a comparison of the monitored response to an expected response, wherein the parameter defining the health metric describes a confidence level in a validity of one of the one or more relationships.
8. The one or more non-transitory computer-readable storage media of claim 7, wherein the node or edge is generated based upon the confidence level in the validity of the one or more relationships.
9. The one or more non-transitory computer-readable storage media of claim 7, wherein the instructions, when executed, further cause the one or more processors to control another building asset based upon the monitored response to at least one of the identified building asset, the one or more first building assets, or the one or more second building assets.
10. The one or more non-transitory computer-readable storage media of claim 7, wherein generating the confidence level includes analyzing a covariance between sensor measurements of the identified building asset or the one or more second building assets associated with the change in the environmental parameter of the building.
11. The one or more non-transitory computer-readable storage media of claim 7, wherein the digital representation includes a graph data structure and wherein the instructions, when executed, further cause the one or more processors to update one or more portions of the graph data structure to represent a relationship.
12. The one or more non-transitory computer-readable storage media of claim 7, wherein the digital representation includes a graph data structure and wherein the one or more processors are further configured to compare versions of the graph data structure representing at least a portion of the building to identify changes between the versions.
13. The one or more non-transitory computer-readable storage media of claim 12, wherein the identified building asset is selected based on an identified change between the versions of the graph data structure.
14. A building management system (BMS) comprising a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to: identify a building asset within a digital representation of a building having nodes representative of building assets within the building and edges describing one or more relationships of the building asset within the building;select and execute, based on a type of data monitored by the building asset, a predetermined control action for one or more other building assets included within the digital representation to change an environmental parameter of the building;monitor a response of at least one of the building asset or one of the one or more other building assets to the predetermined control action; andupdate the digital representation to include a parameter defining a health metric of a node or edge associated with the building asset based on a comparison of the monitored response to the predetermined control action to an expected response, wherein the parameter defining the health metric describes a confidence level in a validity of one of the one or more relationships.
15. The building management system (BMS) of claim 14, wherein the node or edge is generated based upon the confidence level in the validity of the one or more relationships.
16. The building management system (BMS) of claim 14, wherein the processing circuit is further configured to execute another predetermined control action based upon the monitored response to the predetermined control action.
17. The building management system (BMS) of claim 14, wherein generating the confidence level includes analyzing a covariance between sensor measurements of the one or more other building assets associated with the change in the environmental parameter of the building.
18. The building management system (BMS) of claim 14, wherein the digital representation includes a graph data structure and wherein the processing circuit is further configured to update one or more portions of the graph data structure to represent a relationship of the building asset.
19. The building management system (BMS) of claim 14, wherein the digital representation includes a graph data structure and wherein the processing circuit is further configured to compare versions of the graph data structure representing at least a portion of the building to identify changes between the versions, and wherein identifying the building asset includes selecting an asset based on an identified change between the versions of the graph data structure.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

The present application claims the benefit and priority of U.S. Provisional Patent Application No. 63/108,137 filed on Oct. 30, 2020, the entire disclosure of which is incorporated by reference herein.

US Referenced Citations (529)

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
7243054	Rappaport et al.	Jul 2007	B2
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
8145995	Rohrabaugh et al.	Mar 2012	B2
8229470	Ranjan et al.	Jul 2012	B1
8401991	Wu et al.	Mar 2013	B2
8495745	Schrecker et al.	Jul 2013	B1
8516016	Park et al.	Aug 2013	B2
8525828	Bates	Sep 2013	B1
8532808	Drees et al.	Sep 2013	B2
8532839	Drees et al.	Sep 2013	B2
8600556	Nesler et al.	Dec 2013	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
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
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
9652813	Gifford et al.	May 2017	B2
9703276	Piaskowski et al.	Jul 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
10055114	Shah et al.	Aug 2018	B2
10055206	Park et al.	Aug 2018	B2
10116461	Fairweather et al.	Oct 2018	B2
10139792	Schmitt	Nov 2018	B2
10149141	Stamatakis et al.	Dec 2018	B1
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
10345772	Piaskowski et al.	Jul 2019	B2
10514963	Shrivastava et al.	Dec 2019	B2
10515098	Park et al.	Dec 2019	B2
10527306	Boettcher et al.	Jan 2020	B2
10534326	Sridharan et al.	Jan 2020	B2
10536295	Fairweather et al.	Jan 2020	B2
10558183	Piaskowski et al.	Feb 2020	B2
10564993	Deutsch et al.	Feb 2020	B2
10649419	Asp et al.	May 2020	B2
10659919	Li et al.	May 2020	B1
10684033	Sinha et al.	Jun 2020	B2
10705492	Harvey	Jul 2020	B2
10708078	Harvey	Jul 2020	B2
10739733	Saxena et al.	Aug 2020	B1
10760815	Janakiraman et al.	Sep 2020	B2
10762475	Song et al.	Sep 2020	B2
10775988	Narain et al.	Sep 2020	B2
10798175	Knight et al.	Oct 2020	B1
10824120	Ahmed	Nov 2020	B2
10845771	Harvey	Nov 2020	B2
10853717	Abramson et al.	Dec 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
10921973	Park et al.	Feb 2021	B2
10956842	Wenzel et al.	Mar 2021	B2
10969133	Harvey	Apr 2021	B2
10986121	Stockdale et al.	Apr 2021	B2
10997325	Austern et al.	May 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
11049317	Besecker et al.	Jun 2021	B2
11054796	Holaso	Jul 2021	B2
11070390	Park et al.	Jul 2021	B2
11073976	Park et al.	Jul 2021	B2
11087054	Austern et al.	Aug 2021	B2
11108587	Park et al.	Aug 2021	B2
11113295	Park et al.	Sep 2021	B2
11190578	Kitchen et al.	Nov 2021	B2
11221601	Chakraborty et al.	Jan 2022	B1
11229138	Harvey et al.	Jan 2022	B1
11272011	Laughton et al.	Mar 2022	B1
11275870	Diguet et al.	Mar 2022	B2
11314726	Park et al.	Apr 2022	B2
11314788	Park et al.	Apr 2022	B2
11392736	Austern et al.	Jul 2022	B2
11473804	Chen et al.	Oct 2022	B2
11499738	Ratakonda et al.	Nov 2022	B2
11520955	Levy et al.	Dec 2022	B2
11556105	Cooley et al.	Jan 2023	B2
11561522	Cooley et al.	Jan 2023	B2
11561523	Cooley et al.	Jan 2023	B2
11567466	Galvez et al.	Jan 2023	B1
11573551	Cooley et al.	Feb 2023	B2
11586167	Cooley et al.	Feb 2023	B2
20020010562	Schleiss et al.	Jan 2002	A1
20020016639	Smith et al.	Feb 2002	A1
20020059229	Natsumeda et al.	May 2002	A1
20020091738	Rohrabaugh et al.	Jul 2002	A1
20020123864	Eryurek et al.	Sep 2002	A1
20020147506	Eryurek et al.	Oct 2002	A1
20020177909	Fu et al.	Nov 2002	A1
20030005486	Ridolfo et al.	Jan 2003	A1
20030014130	Grumelart	Jan 2003	A1
20030073432	Meade, II	Apr 2003	A1
20030158704	Triginai 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
20050055308	Meyer et al.	Mar 2005	A1
20050108262	Fawcett et al.	May 2005	A1
20050131657	Sean Mei	Jun 2005	A1
20050154494	Ahmed	Jul 2005	A1
20050278703	Lo et al.	Dec 2005	A1
20050283337	Sayal	Dec 2005	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
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
20070261062	Bansal et al.	Nov 2007	A1
20070273497	Kuroda et al.	Nov 2007	A1
20070273610	Baillot	Nov 2007	A1
20080015823	Arnold et al.	Jan 2008	A1
20080034425	Overcash et al.	Feb 2008	A1
20080094230	Mock et al.	Apr 2008	A1
20080097816	Freire et al.	Apr 2008	A1
20080186160	Kim et al.	Aug 2008	A1
20080249756	Chaisuparasmikul	Oct 2008	A1
20080252723	Park	Oct 2008	A1
20080281472	Podgorny et al.	Nov 2008	A1
20090195349	Frader-Thompson et al.	Aug 2009	A1
20100045439	Tak et al.	Feb 2010	A1
20100058248	Park	Mar 2010	A1
20100131533	Ortiz	May 2010	A1
20100262313	Chambers	Oct 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
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
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
20120135759	Imes et al.	May 2012	A1
20120136485	Weber et al.	May 2012	A1
20120143516	Mezic 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
20120310602	Jacobi et al.	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
20130204836	Choi et al.	Aug 2013	A1
20130246916	Reimann et al.	Sep 2013	A1
20130247205	Schrecker et al.	Sep 2013	A1
20130257850	Chen et al.	Oct 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
20130303193	Dharwada et al.	Nov 2013	A1
20130331995	Rosen	Dec 2013	A1
20130338970	Reghetti	Dec 2013	A1
20140018940	Casilli	Jan 2014	A1
20140032506	Hoey et al.	Jan 2014	A1
20140035726	Schoner et al.	Feb 2014	A1
20140039685	Blount	Feb 2014	A1
20140059483	Mairs et al.	Feb 2014	A1
20140081652	Klindworth	Mar 2014	A1
20140135952	Maehara	May 2014	A1
20140152651	Chen et al.	Jun 2014	A1
20140172184	Schmidt et al.	Jun 2014	A1
20140180646	Diguet 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
20140327670	Chen et al.	Nov 2014	A1
20150019174	Kiff et al.	Jan 2015	A1
20150042240	Aggarwal et al.	Feb 2015	A1
20150105917	Sasaki et al.	Apr 2015	A1
20150145468	Ma et al.	May 2015	A1
20150156031	Fadell et al.	Jun 2015	A1
20150168931	Jin	Jun 2015	A1
20150169791	Lavrov et al.	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
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
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
20160048312	Holaso	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
20160139752	Shim et al.	May 2016	A1
20160163186	Davidson et al.	Jun 2016	A1
20160170390	Xie 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
20160266771	Kittredge et al.	Sep 2016	A1
20160305678	Pavlovski et al.	Oct 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
20170046012	Han 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
20170090441	Schmitt	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
20170115642	Sridharan 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
20170208151	Gil et al.	Jul 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
20170300214	Roundtree	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
20170329292	Piaskowski 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
20170357738	Garske et al.	Dec 2017	A1
20170357908	Cabadi et al.	Dec 2017	A1
20180011620	Troy et al.	Jan 2018	A1
20180012159	Kozloski et al.	Jan 2018	A1
20180013579	Fairweather et al.	Jan 2018	A1
20180024520	Sinha et al.	Jan 2018	A1
20180039238	Gaertner et al.	Feb 2018	A1
20180046173	Ahmed	Feb 2018	A1
20180048485	Pelton et al.	Feb 2018	A1
20180069932	Tiwari et al.	Mar 2018	A1
20180088789	Han 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
20180218540	Sridharan et al.	Aug 2018	A1
20180239982	Rutschman et al.	Aug 2018	A1
20180259209	Przybylski	Sep 2018	A1
20180259934	Piaskowski	Sep 2018	A1
20180268319	Guo 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
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
20190012728	Horn	Jan 2019	A1
20190013023	Pourmohammad et al.	Jan 2019	A1
20190025771	Park et al.	Jan 2019	A1
20190033799	Billings	Jan 2019	A1
20190037135	Hedge	Jan 2019	A1
20190042988	Brown et al.	Feb 2019	A1
20190088106	Grundstrom	Mar 2019	A1
20190094824	Xie et al.	Mar 2019	A1
20190096217	Pourmohammad et al.	Mar 2019	A1
20190102840	Perl et al.	Apr 2019	A1
20190121801	Jethwa et al.	Apr 2019	A1
20190138512	Pourmohammad et al.	May 2019	A1
20190147883	Mellenthin et al.	May 2019	A1
20190158309	Park et al.	May 2019	A1
20190163152	Worrall et al.	May 2019	A1
20190196784	Sheffer	Jun 2019	A1
20190208018	Scanlin et al.	Jul 2019	A1
20190265659	Piaskowski et al.	Aug 2019	A1
20190268178	Fairweather et al.	Aug 2019	A1
20190310979	Masuzaki et al.	Oct 2019	A1
20190347381	Benjamin et al.	Nov 2019	A1
20190369575	Raina et al.	Dec 2019	A1
20190377306	Harvey	Dec 2019	A1
20190384242	Holaso	Dec 2019	A1
20200026806	Ullom	Jan 2020	A1
20200044887	Jayanath Wewalaarachchi et al.	Feb 2020	A1
20200047343	Bal et al.	Feb 2020	A1
20200090085	Martinez Canedo et al.	Mar 2020	A1
20200104530	Park et al.	Apr 2020	A1
20200106633	Park et al.	Apr 2020	A1
20200124466	Lubas et al.	Apr 2020	A1
20200125043	Park	Apr 2020	A1
20200159173	Goyal	May 2020	A1
20200159182	Goyal	May 2020	A1
20200159376	Goyal	May 2020	A1
20200159723	Goyal	May 2020	A1
20200162354	Drees et al.	May 2020	A1
20200188718	Trivelpiece et al.	Jun 2020	A1
20200202136	Shrestha et al.	Jun 2020	A1
20200225623	Vass	Jul 2020	A1
20200226156	Borra et al.	Jul 2020	A1
20200226223	Reichl	Jul 2020	A1
20200233680	Ma et al.	Jul 2020	A1
20200272114	Grabowski et al.	Aug 2020	A1
20200284457	Sinha et al.	Sep 2020	A1
20200285203	Thakur et al.	Sep 2020	A1
20200285855	Brebner	Sep 2020	A1
20200304375	Chennai et al.	Sep 2020	A1
20200336328	Harvey	Oct 2020	A1
20200348632	Harvey	Nov 2020	A1
20200379140	Kostrun et al.	Dec 2020	A1
20200387576	Brett et al.	Dec 2020	A1
20200387643	Lee	Dec 2020	A1
20200396208	Brett et al.	Dec 2020	A1
20210042299	Migliori	Feb 2021	A1
20210043221	Yelchuru et al.	Feb 2021	A1
20210055716	Turner	Feb 2021	A1
20210073430	Levy et al.	Mar 2021	A1
20210073433	Austern et al.	Mar 2021	A1
20210073449	Segev et al.	Mar 2021	A1
20210200807	Ploegert et al.	Jul 2021	A1
20210208002	Meggers et al.	Jul 2021	A1
20210256583	Jacobs et al.	Aug 2021	A1
20210325070	Endel et al.	Oct 2021	A1
20210342961	Winter et al.	Nov 2021	A1
20210365602	Gifford	Nov 2021	A1
20210381711	Harvey et al.	Dec 2021	A1
20210381712	Harvey et al.	Dec 2021	A1
20210382445	Harvey et al.	Dec 2021	A1
20210382449	Krishnan et al.	Dec 2021	A1
20210382451	Bharathi et al.	Dec 2021	A1
20210382452	Krishnan et al.	Dec 2021	A1
20210382474	Krishnan 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
20210389968	Majewski 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
20220092227	Yin et al.	Mar 2022	A1
20220121965	Chatterji et al.	Apr 2022	A1
20220138684	Harvey	May 2022	A1
20220147000	Cooley et al.	May 2022	A1
20220150124	Cooley et al.	May 2022	A1
20220215264	Harvey et al.	Jul 2022	A1
20220268470	Hriljac et al.	Aug 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
20230169220	Ramanasankaran et al.	Jun 2023	A1
20230185983	Ramanasankaran et al.	Jun 2023	A1
20230213901	Galvez et al.	Jul 2023	A1
20230213902	Galvez et al.	Jul 2023	A1
20230213903	Galvez et al.	Jul 2023	A1
20230214555	Harvey et al.	Jul 2023	A1
20230216742	Galvez et al.	Jul 2023	A1
20230217361	Galvez et al.	Jul 2023	A1
20230252205	Harvey et al.	Aug 2023	A1

Foreign Referenced Citations (47)

Number	Date	Country
2019226217	Nov 2020	AU
2019226264	Nov 2020	AU
101415011	Apr 2009	CN
102136099	Jul 2011	CN
102136100	Jul 2011	CN
102650876	Aug 2012	CN
104040583	Sep 2014	CN
104603832	May 2015	CN
104919484	Sep 2015	CN
106204392	Dec 2016	CN
106406806	Feb 2017	CN
106960269	Jul 2017	CN
107147639	Sep 2017	CN
107598928	Jan 2018	CN
109824100	May 2019	CN
2 528 033	Nov 2012	EP
3 268 821	Jan 2018	EP
3 324 306	May 2018	EP
4 226 263	Aug 2023	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
2013-152618	Aug 2013	JP
2014-044457	Mar 2014	JP
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-2020061621	Apr 2020	WO
WO-2022042925	Mar 2022	WO
WO-2022103812	May 2022	WO
WO-2022103813	May 2022	WO
WO-2022103820	May 2022	WO
WO-2022103822	May 2022	WO
WO-2022103824	May 2022	WO
WO-2022103829	May 2022	WO
WO-2022103831	May 2022	WO

Non-Patent Literature Citations (81)

Entry
Rao; English translation corresponding to Chinese Patent Application CN 109824100 A; May 31, 2019. (Year: 2019).
International Search Report and Written Opinion on PCT/US2017/013647, dated Apr. 18, 2017, 10 pages.
U.S. Appl. No. 16/885,959, Johnson Controls Technology Co.
U.S. Appl. No. 16/885,968, Johnson Controls Technology Co.
“OpenRefine: A free, open source, powerful tool for working with messy data,” URL: https://openrefine.org/, retrieved Feb. 11, 2021, 4 pages.
International Search Report and Written Opinion for PCT Appl. Ser. No. PCT/US2021/057360 dated Feb. 1, 2022 (14 pages).
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.
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. 12, 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, Jue 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, mailed Oct. 5, 2017, 11 pages.
International Search Report and Written Opinion on PCT/US2017/052633, mailed Oct. 23, 2017, 9 pages.
International Search Report and Written Opinion on PCT/US2017/052829, mailed Nov. 27, 2017, 24 pages.
International Search Report and Written Opinion on PCT/US2018/024068, mailed 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, mailed Dec. 19, 2018, 13 pages.
International Search Report and Written Opinion on PCT/US2018/052975, mailed Jan. 2, 2019, 13 pages.
International Search Report and Written Opinion on PCT/US2018/052994, mailed Jan. 7, 2019, 15 pages.
International Search Report and Written Opinion on PCT/US2019/015481, dated May 17, 2019, 15 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, First ed. published 2020 (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, 2023 (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. 18/375,153, filed Sep. 29, 2023, Namburi et al.

Related Publications (1)

	Number	Date	Country
	20220137570 A1	May 2022	US

Provisional Applications (1)

	Number	Date	Country
	63108137	Oct 2020	US

Building management system with auto-configuration using existing points

Information

Patent Number

Date Filed

Date Issued

Inventors

Original Assignees

Examiners

Agents

CPC

Field of Search

US

International Classifications

Term Extension

Abstract