# US 12,200,062 B1 · IoT contextually-aware digital twin with enhanced discovery > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,200,062 B1 - Document ID: US12200062B1 - Status: Granted - Kind: B1 - Application number: 18/386,064 - Filed: 2023-11-01 - Granted: 2025-01-14 - Earliest claimed: 2019-06-28 - Assignees: CohesionIB - Applicants: cohesionIB, Inc. - Inventors: Robert Knight; Nikunj Patel; Rick Szcodronski - Web page: https://patentgrep.com/patent/US12200062B1 ## CPC - G05B 19/0426: {Programming the control sequence} - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - G05B 15/02: electric - G05B 19/048: Monitoring; Safety - H04L 67/1065: {Discovery involving distributed pre-established resource-based relationships among peers, e.g. based on distributed hash tables [DHT] (pre-configuration of logical or physical connections H04L67/1053)} - H04L 67/51: Discovery or management thereof, e.g. service location protocol [SLP] or web services - G05B 2219/2614: HVAC, heating, ventillation, climate control - G05B 2219/2642: Domotique, domestic, home control, automation, smart house - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract IoT devices within a commercial real-estate or residential building environment may be connected through networks, such as a Building Automation and Control network (BACnet). Systems and methods according to this disclosure provide automatic discovery of IoT devices and relationships in commercial real-estate and residential buildings and integration of the BACnet devices into the digital twin of the building. In some implementations, an IoT gateway is configured to translate the communication received from the BACnet to an IoT cloud platform and configured to normalize the data across the different security platforms into a consistent format which enables integration and interoperability of the different building system platforms that may otherwise be operating in isolation from each other. Systems and methods according to the present disclosure provide edge based analytics and control of IoT BACnet devices based on defining conditions and rules, and provide integration of multiple building systems in the context of commercial real-estate and residential buildings. ## Claims ### Claim 1 A method of contextually-aware digital twin generation comprising: training a machine learning algorithm on building data associated respectively with a plurality of commercial real estate buildings and on a plurality of digital twins respectively associated with the plurality of commercial real estate buildings; generating information about relationships for devices based on the trained machine learning algorithm, wherein the relationships are between physical entities of a first commercial real estate building; inserting the information about the relationships into a device library; receiving, by a computing device, device data about an asset comprising one or more devices residing in one or more physical spaces associated with the first commercial real estate building; storing, by the computing device, the device data about the one or more devices in the device library; discovering, based on a received identification of a discovered device connected to an associated network, whether the discovered device is stored in a graph representation of the first commercial real estate building; accessing the device data from the device library, wherein the device data includes the information about the relationships; based on the device data from the device library being known about the discovered device, identifying device information about the discovered device from the accessed device data; based on the discovered device not being stored in the graph representation, modifying the graph representation to include the discovered device with the identified device information, wherein modifying the graph representation comprises: determining at least one relationship with one or more assets of the first commercial real estate building; validating, using one or more asset templates that are based on the trained machine learning algorithm, the at least one relationship with the one or more assets; and based on successfully validating the at least one relationship, modeling the at least one relationship in the graph representation of the first commercial real estate building. ### Claim 2 (depends on 1) The method of claim 1, wherein the identifying comprises predicting a relationship between the discovered device and another entity of the graph representation of the first commercial real estate building. ### Claim 3 (depends on 1) The method of claim 1, further comprising: including manufacturer information in the stored device data at the device library. ### Claim 4 (depends on 3) The method of claim 3, further comprising: including model information in the stored device data at the device library. ### Claim 5 (depends on 3) The method of claim 3, further comprising: obtaining a manufacturer identification of the discovered device; and accessing the device information based on the manufacturer identification. ### Claim 6 (depends on 4) The method of claim 4, further comprising: obtaining a model identification of the discovered device; and accessing the device information based on the model identification. ### Claim 7 (depends on 2) The method of claim 2, further comprising: determining at least one controller associated with at least one or more points of the discovered device. ### Claim 8 (depends on 1) The method of claim 1, further comprising: training the machine learning algorithm with the received device data on the contextually-aware digital twin. ### Claim 9 (depends on 1) The method of claim 1, further comprising: determining one or more points based on the device information. ### Claim 10 (depends on 1) The method of claim 1, wherein the machine learning algorithm is performed on contextually-aware digital twins of at least one other commercial real estate building. ### Claim 11 (depends on 1) The method of claim 1, wherein the relationship is between a controller and at least one device. ### Claim 12 (depends on 1) The method of claim 1 further comprising: executing the machine learning algorithm on the contextually-aware digital twin of the first commercial real estate building to provide a recommendation for a second commercial real estate building, wherein the first commercial real estate building and the second commercial real estate building are different. ### Claim 13 (depends on 1) The method of claim 1 further comprising: collecting data across a plurality of commercial real estate buildings; and training the machine learning algorithm on the collected data. ### Claim 14 (depends on 13) The method of claim 13 further comprising: detecting at least one pattern from the collected data; and generating a recommendation for a third commercial real estate building based on the detected at least one pattern. ### Claim 15 (depends on 13) The method of claim 13, wherein the collected data is crowd sourced. ### Claim 16 A system for contextually-aware digital twin generation comprising: one or more processors; and a memory storing computer-readable instructions that, when executed by the one or more processors, configure the one or more processors to: train a machine learning algorithm on building data associated respectively with a plurality of commercial real estate buildings and on a plurality of digital twins respectively associated with the plurality of commercial real estate buildings; generate information about relationships for devices based on the trained machine learning algorithm, wherein the relationships are between physical entities of a first commercial real estate building; insert the information about the relationships into a device library; receive device data about an asset comprising one or more devices residing in one or more physical spaces associated with the first commercial real estate building; store the device data about the one or more devices in the device library; discover, based on a received identification of a discovered device connected to an associated network, whether the discovered device is stored in a graph representation of the first commercial real estate building; access the device data from the device library, wherein the device data includes the information about the relationship; based on the device data from the device library being known about the discovered device, identify device information about the discovered device from the accessed device data; based on the discovered device not being stored in the graph representation, modify the graph representation to include the discovered device with the identified device information, wherein modification of the graph representation comprises: determining at least one relationship with one or more assets of the first commercial real estate building; validating, using one or more asset templates that are based on the trained machine learning algorithm, the at least one relationship with the one or more assets; and based on successfully validating the at least one relationship, modeling the at least one relationship in the graph representation of the first commercial real estate building. ### Claim 17 (depends on 16) The system of claim 16, wherein the memory storing computer-readable instructions that, when executed by the one or more processors, configure the one or more processors to: predict a relationship between the discovered device and another entity of the graph representation of the first commercial real estate building; and determine at least one controller associated with at least one or more points of the discovered device. ### Claim 18 (depends on 16) The system of claim 16, wherein the memory storing computer-readable instructions that, when executed by the one or more processors, configure the one or more processors to: include manufacturer and model information in the stored device data at the device library; obtain a manufacturer and model identification of the discovered device; and access the device information based on the manufacturer and model identification. ### Claim 19 (depends on 16) The system of claim 16, wherein the memory storing computer-readable instructions that, when executed by the one or more processors, configure the one or more processors to: train the machine learning algorithm with the received device data on the contextually-aware digital twin. ### Claim 20 (depends on 16) The system of claim 16, wherein the memory storing computer-readable instructions that, when executed by the one or more processors, configure the one or more processors to: determine one or more points based on the device information. ### Claim 21 A non-transitory machine-readable storage medium comprising machine-readable instructions for causing a processor to execute a method of contextually-aware digital twin generation comprising: training a machine learning algorithm on building data associated respectively with a plurality of commercial real estate buildings and on a plurality of digital twins respectively associated with the plurality of commercial real estate buildings; generating information about relationships for devices based on the trained machine learning algorithm, wherein the relationships are between physical entities of a first commercial real estate building; inserting the information about the relationships into a device library; receiving device data about an asset comprising one or more devices residing in one or more physical spaces associated with the first commercial real estate building; storing the device data about the one or more devices in the device library; discovering, based on a received identification of a discovered device connected to an associated network, whether the discovered device is stored in a graph representation of the first commercial real estate building; accessing the device data from the device library, wherein the device data includes the information about the relationships; based on the device data from the device library being known about the discovered device, identifying device information about the discovered device from the accessed device data; based on the discovered device not being stored in the graph representation, modifying the graph representation to include the discovered device with the identified device information, wherein modifying the graph representation comprises: determining at least one relationship with one or more assets of the first commercial real estate building; validating, using one or more asset templates that are based on the trained machine learning algorithm, the at least one relationship with the one or more assets; and based on successfully validating the at least one relationship, modeling the at least one relationship in the graph representation of the first commercial real estate building. ### Claim 22 (depends on 21) The non-transitory machine-readable storage medium of claim 21, wherein the machine-readable instructions cause the processor to execute the method comprising: predicting a relationship between the discovered device and another entity of the graph representation of the first commercial real estate building; and determining at least one controller associated with at least one or more points of the discovered device. ### Claim 23 (depends on 21) The non-transitory machine-readable storage medium of claim 21, wherein the machine-readable instructions cause the processor to execute the method comprising: including manufacturer and model information in the stored device data at the device library; obtaining a manufacturer and model identification of the discovered device; and accessing the device information based on the manufacturer and model identification. ### Claim 24 (depends on 21) The non-transitory machine-readable storage medium of claim 21, wherein the machine-readable instructions cause the processor to execute the method comprising: training the machine learning algorithm with the received device data on the contextually-aware digital twin.