DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d), with respect to Indian parent Application No. IN202341015968 filed on 3/10/2023.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
…a recommendation engine… (Claims 1, 8 and 14),
…a ranking engine… or a resolution engine… (Claim 10).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-20 are directed to statutory categories, namely a process (claims 1-7), a machine (claims 8-13) and an article of manufacture (claims 14-20).
Step 2A, Prong 1: Claims 1, 8 and 14 in part, recite the following abstract idea:
…A method, comprising: receiving, by … information identifying… for energy management, … associated with first energy information, … associated with second energy information, and a set of software entities associated with third energy information; generating, by … using the first energy information for simulation of… the second energy information for simulation of…, and the third energy information for simulation of the set of software entities; determining, by… , a set of energy consumption metrics, for … associated with a set of candidate parameters; generating, by … and using … one or more recommendations for … based on the set of energy consumption metrics associated with the set of candidate parameters; and transmitting, by … information associated with identifying the one or more recommendations [Claim 1],
…configured to: receive information identifying… for energy management, …associated with first energy information, … associated with second energy information, and a set of software entities associated with third energy information; generate… using the first energy information for simulation of the set of hardware components, the second energy information for simulation of …, and the third energy information for simulation of the set of software entities; determine, using … a set of energy consumption metrics, for … associated with a set of candidate parameters; generate, using … one or more recommendations for … based on the set of energy consumption metrics associated with the set of candidate parameters; and transmit information associated with implementing the one or more recommendations [Claim 8],
…receive information identifying a computing system for energy management, … associated with first energy information, … associated with second energy information, and a set of software entities associated with third energy information; generate … using the first energy information for simulation of …the second energy information for simulation of … and the third energy information for simulation of the set of software entities; determine, using …a set of energy consumption metrics, for … associated with a set of candidate parameters; generate, using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters; and determine a set of updated energy consumption metrics associated with the set of candidate parameters based on the one or more recommendations; and select a particular recommendation, from the one or more recommendations, based on the set of updated energy consumption metrics; and transmit information identifying the particular recommendation.
These concepts are not meaningfully different than the following concepts identified by the MPEP:
Concepts relating to certain methods of organizing human activity. The aforementioned limitations describe steps for managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions. Specifically, determining energy consumption metrics and providing recommendations describes steps for managing personal behavior.
The aforementioned limitations further describe steps for fundamental economic principles or practices which includes hedging, insurance, and mitigating risk. Specifically, determining energy consumption metrics and providing recommendations describes steps for mitigating risk of excessive energy consumption. As such, claims 1, 8 and 14 recite concepts identified as abstract ideas.
The dependent claims recite limitations relative to the independent claims, including, for example:
wherein generating …comprises: modeling one or more entities associated with at least one of: a service characteristic, a project characteristic, a release characteristic, a code characteristic, or an energy characteristic. [Claim 2],
…wherein generating … comprises: identifying a set of energy providers for the computing system and a set of carbon intensity estimates associated with the set of energy providers [Claim 3],
…wherein determining the set of energy consumption metrics comprises: determining a set of emissions metrics associated with … [Claim 4],
…wherein determining the set of emissions metrics comprises: determining a software carbon intensity associated with a computing task performable by… [Claim 5],
…wherein determining the set of emissions metrics comprises: generating a set of benchmarking scores for the set of emissions metrics, a benchmarking score, of the set of benchmarking scores, identifying a relative position of a corresponding emission metric, of the set of emissions metrics, in a range of candidate values for the corresponding emission metric [Claim 6],
The limitations of these dependent claims are merely narrowing the abstract idea identified in the independent claims, and thus, the dependent claims also recite abstract ideas.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. In particular, claims 1, 8 and 14 only recite the following additional elements –
…a device… a computing system… the computing system having a set of hardware components… a set of virtual machines…; … the device, a digital twin of the computing system… the set of hardware components… the set of virtual machines…; … the device and using the digital twin of the computing system… the computing system…; …the device… a recommendation engine… the computing system…; …the device… [Claim 1],
… A device for wireless communication, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories… a computing system… the computing system having a set of hardware components… a set of virtual machines…; … a digital twin of the computing system… the set of hardware components… the set of virtual machines…; … the digital twin of the computing system… the computing system…; …the device… a recommendation engine… the computing system… [Claim 8],
A non-transitory computer-readable medium storing a set of instructions for wireless communication, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to… a computing system… the computing system having a set of hardware components… a set of virtual machines…; …a digital twin of the computing system… the set of hardware components… the set of virtual machines…; …the digital twin of the computing system… the computing system…; …a recommendation engine… the computing system… [Claim 14].
The apparatus and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following MPEP example:
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Furthermore, the computer implemented element is considered to amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Accordingly, these additional elements do not integrate the abstract idea into a practical application.
The remaining dependent claims do not recite any new additional elements, and thus do not integrate the abstract idea into a practical application.
Step 2B: Claims 1, 8 and 14 and their underlying limitations, steps, features and terms, considered both individually and as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reasons:
…a device… a computing system… the computing system having a set of hardware components… a set of virtual machines…; … the device, a digital twin of the computing system… the set of hardware components… the set of virtual machines…; … the device and using the digital twin of the computing system… the computing system…; …the device… a recommendation engine… the computing system…; …the device… [Claim 1],
… A device for wireless communication, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories… a computing system… the computing system having a set of hardware components… a set of virtual machines…; … a digital twin of the computing system… the set of hardware components… the set of virtual machines…; … the digital twin of the computing system… the computing system…; …the device… a recommendation engine… the computing system… [Claim 8],
A non-transitory computer-readable medium storing a set of instructions for wireless communication, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to… a computing system… the computing system having a set of hardware components… a set of virtual machines…; …a digital twin of the computing system… the set of hardware components… the set of virtual machines…; …the digital twin of the computing system… the computing system…; …a recommendation engine… the computing system… [Claim 14].
These elements do not amount to significantly more than the abstract idea for the reasons discussed in 2A prong 2 with regard to MPEP 2106.05(a) and MPEP 2106.05(f). By the failure of the elements to integrate the abstract idea into a practical application there, the additional elements likewise fail to amount to an inventive concept that is significantly more than an abstract idea here, in Step 2B.
As such, both individually or in combination, these limitations do not add significantly more to the judicial exception.
The remaining dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the dependent claims do not recite any new additional elements other than those mentioned in the independent claims, which amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). As such, these claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Papadopoulos et al., U.S. Publication No. 2024/0235191 [hereinafter Papadopoulos], in view of Kumar et al., U.S. Publication No. 2021/0334895 [hereinafter Kumar].
Regarding Claim 1, Papadopoulos discloses …A method, comprising: receiving, by a device, information identifying a computing system for energy management, the computing system having a set of hardware components associated with first energy information, a set of virtual machines associated with second energy information, and a set of software entities associated with third energy information (Papadopoulos, ¶ 4, One implementation of the present disclosure is a building automation system (BAS) for a building, according to some embodiments. In some embodiments, the BAS includes multiple devices including processing circuitry. The processing circuitry is configured to obtain time series data indicating reduced energy consumption of building equipment of the building, or green energy generation for the building, according to some embodiments. The processing circuitry is also configured to determine a corresponding carbon emissions reduction resulting from the reduced energy consumption of the building equipment or the green energy generation, according to some embodiments. The processing circuitry is also configured to create a carbon offset token responsive to the corresponding carbon emissions reduction being greater than a threshold, according to some embodiments. The processing circuitry is also configured to validate a new block of a first blockchain, according to some embodiments. In some embodiments, the carbon offset token is an attribute or data of the new block of the first blockchain, the first blockchain being limited from public access. In some embodiments, the processing circuitry is configured to provide the first blockchain as an input to a new block or a sidechain of a second blockchain. In some embodiments, the second blockchain is publicly accessible and includes the carbon offset token as a result of providing the first blockchain as an input to the new block or sidechain of the second blockchain), (Id., ¶ 53, Airside system 130 can deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and can provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) (discloses hardware components associated with first energy information) configured to measure attributes of the supply airflow. AHU 106 can receive input from sensors located within AHU 106 and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone), (Id., ¶ 239, Process 2500 includes obtaining equipment information associated with one or more physical or non-physical building equipment (step 2502), according to some embodiments. The communications interface 2302 can obtain equipment information. The equipment information can include ownership data and technical data. The equipment information can be associated with physical and/or non-physical building equipment. Physical building equipment can include, for example, hardware-based devices having a housing, circuitry, processors, memory, power supplies, and/or other physical hardware (h/w) components. Non-physical building equipment can include, for example, software-based devices such as virtual devices, digital twins, virtual machines, (discloses virtual machines) emulated equipment, device software (s/w) or other equipment that exists in a virtual or non-physical form. The communications interface 2302 can obtain the equipment information from the building agencies server 2330. The communications interface 2302 can provide the equipment information to the NFT generator 2318), (Id., ¶ 48, Using blockchain with HVAC devices can result in higher levels of security for a building management system (BMS) since a single point of failure is removed. If a cyber-attack is launched against the BMS, the cyber-attack would need to compromise the HVAC data chain of all the HVAC devices instead of just the data stored on a central server of the BMS. Using the HVAC data chain can result in a high byzantine fault tolerance. Further, there may be mass disintermediation due to consensus-driven decision-making associated with the HVAC data chain. Further, using the HVAC data chain may lower data corruption security breach uncertainties. Using blockchain can allow the HVAC devices to enter permanent and/or time based contracts or transactions among each other without any human intervention. In a building with HVAC devices, the HVAC devices can use blockchain to store and validate energy consumption information, load curtailment information, carbon credits, (discloses second and third energy information) transmit access rights and licensing information, transmit control actions that can have significant impact on the controlled environment, perform transaction negotiation between two HVAC devices, transport sensitive information across the HVAC device network, and can be used for any communication in building automation and control implementations that have environment requirements that require validation and security. The HVAC data chain can be used for any communication in a BMS (e.g., transport of sensitive information) and/or other control implementation that have validated environment requirements), (Id., ¶ 82, Referring now to FIG. 5, a system 500 of HVAC devices communicating via a network, with the HVAC devices storing an HVAC data chain 510, is shown, according to some embodiments. System 500 is shown to include a number of HVAC devices communicating via a network 508 (e.g., a distributed network). The number of HVAC devices are shown to include HVAC device 1, HVAC device 2, HVAC device 3, HVAC device 4, HVAC device 5, and HVAC device 6. There can be any number of HVAC devices in system 500. HVAC devices 1-6 can be any controller, actuator, or sensor that can communicate via network 508. In some embodiments, HVAC devices 1-6 are building gateways and/or various other building devices or building controllers. HVAC devices 1-6 can be the devices of smart connected things 204 and/or gateway 206 as described with reference to FIGS. 2-3 (e.g., controllers 226a-b, actuators 224, sensors 220, etc.) In some embodiments, HVAC devices 1-6 are VAV boxes of VAV units 116, AHU 106, chiller 102, and/or boiler 104 as described with reference to FIG. 1), (Id., ¶ 83, In some embodiments, HVAC devices 1-6 can be computing devices that utilize Software Agents. Software Agents are described with further reference to U.S. patent application Ser. No. 15/367,167 filed Dec. 1, 2016, the entirety of which is incorporated by reference herein (discloses software entities));
generating, by the device, a digital twin of the computing system using the first energy information for simulation of the set of hardware components, the second energy information for simulation of the set of virtual machines, and the third energy information for simulation of the set of software entities (Id., ¶ 222, In some embodiments, the virtual environment server 2332 can generate a virtual environment (e.g., a digital twin) (discloses digital twin) for the building 10 and/or for the building model that pertains to building 10. For example, the virtual environment server 2332 can receive, from the communications interface 2302, the building model 2322. The virtual environment server 2332 can, using the building model 2322, generate the virtual environment for the building 10. The virtual environment server 2332 can include at least electronic device 2334. The electronic device 2334 can be or include at least one of a Head-Mounted Display (HMD), a pair of smart glasses, a mobile device, a virtual reality (VR) headset and/or any other computing device that a user can use to view or interact with the Metaverse), (Id., ¶ 82, Referring now to FIG. 5, a system 500 of HVAC devices communicating via a network, with the HVAC devices storing an HVAC data chain 510, is shown, according to some embodiments. System 500 is shown to include a number of HVAC devices communicating via a network 508 (e.g., a distributed network). The number of HVAC devices are shown to include HVAC device 1, HVAC device 2, HVAC device 3, HVAC device 4, HVAC device 5, and HVAC device 6. There can be any number of HVAC devices in system 500. HVAC devices 1-6 can be any controller, actuator, or sensor that can communicate via network 508. In some embodiments, HVAC devices 1-6 are building gateways and/or various other building devices or building controllers. HVAC devices 1-6 can be the devices of smart connected things 204 and/or gateway 206 as described with reference to FIGS. 2-3 (e.g., controllers 226a-b, actuators 224, sensors 220, etc.) In some embodiments, HVAC devices 1-6 are VAV boxes of VAV units 116, AHU 106, chiller 102, and/or boiler 104 as described with reference to FIG. 1), (Id., ¶ 53, Airside system 130 can deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and can provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) (discloses hardware components associated with first energy information) configured to measure attributes of the supply airflow. AHU 106 can receive input from sensors located within AHU 106 and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone), (Id., ¶ 53, Airside system 130 can deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and can provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) (discloses hardware components associated with first energy information) configured to measure attributes of the supply airflow. AHU 106 can receive input from sensors located within AHU 106 and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone), (Id., ¶ 239, Process 2500 includes obtaining equipment information associated with one or more physical or non-physical building equipment (step 2502), according to some embodiments. The communications interface 2302 can obtain equipment information. The equipment information can include ownership data and technical data. The equipment information can be associated with physical and/or non-physical building equipment. Physical building equipment can include, for example, hardware-based devices having a housing, circuitry, processors, memory, power supplies, and/or other physical hardware (h/w) components. Non-physical building equipment can include, for example, software-based devices such as virtual devices, digital twins, virtual machines, (discloses virtual machines) emulated equipment, device software (s/w) or other equipment that exists in a virtual or non-physical form. The communications interface 2302 can obtain the equipment information from the building agencies server 2330. The communications interface 2302 can provide the equipment information to the NFT generator 2318), (Id., ¶ 48, Using blockchain with HVAC devices can result in higher levels of security for a building management system (BMS) since a single point of failure is removed. If a cyber-attack is launched against the BMS, the cyber-attack would need to compromise the HVAC data chain of all the HVAC devices instead of just the data stored on a central server of the BMS. Using the HVAC data chain can result in a high byzantine fault tolerance. Further, there may be mass disintermediation due to consensus-driven decision-making associated with the HVAC data chain. Further, using the HVAC data chain may lower data corruption security breach uncertainties. Using blockchain can allow the HVAC devices to enter permanent and/or time based contracts or transactions among each other without any human intervention. In a building with HVAC devices, the HVAC devices can use blockchain to store and validate energy consumption information, load curtailment information, carbon credits, (discloses second and third energy information) transmit access rights and licensing information, transmit control actions that can have significant impact on the controlled environment, perform transaction negotiation between two HVAC devices, transport sensitive information across the HVAC device network, and can be used for any communication in building automation and control implementations that have environment requirements that require validation and security. The HVAC data chain can be used for any communication in a BMS (e.g., transport of sensitive information) and/or other control implementation that have validated environment requirements), (Id., ¶ 83, In some embodiments, HVAC devices 1-6 can be computing devices that utilize Software Agents. Software Agents are described with further reference to U.S. patent application Ser. No. 15/367,167 filed Dec. 1, 2016, the entirety of which is incorporated by reference herein (discloses software entities));
determining, by the device and using the digital twin of the computing system, a set of energy consumption metrics, for the computing system, associated with a set of candidate parameters (Id., ¶ 222, In some embodiments, the virtual environment server 2332 can generate a virtual environment (e.g., a digital twin) (discloses digital twin) for the building 10 and/or for the building model that pertains to building 10. For example, the virtual environment server 2332 can receive, from the communications interface 2302, the building model 2322. The virtual environment server 2332 can, using the building model 2322, generate the virtual environment for the building 10. The virtual environment server 2332 can include at least electronic device 2334. The electronic device 2334 can be or include at least one of a Head-Mounted Display (HMD), a pair of smart glasses, a mobile device, a virtual reality (VR) headset and/or any other computing device that a user can use to view or interact with the Metaverse), (Id., ¶ 86, HVAC devices 1-6 can monitor energy consumption, (discloses energy consumption metrics within HVAC device parameters) control lighting systems, control HVAC systems, or perform other functions within the BMS. HVAC devices 1-6 can be configured to optimize the systems they control based on energy efficiency, occupants comfort and productivity, and corporate or regulatory policies. HVAC devices 1-6 may be various HVAC devices, building lighting devices, building security devices (e.g., cameras, access control, etc.), safety devices (e.g., devices of a fire system), and/or devices of any other system of a building (e.g., building 10). In some embodiments, using temperature information from a room and/or zone as well as temperature data from an outside sensor, HVAC devices 1-6 can be configured to affect environmental changes in the room and/or zone. In some embodiments, HVAC devices 1-6 can cause dampers (e.g., actuators 526a-c) located in an air duct to be opened or closed, can activate heating, cooling, and/or air circulation equipment in the building and/or zone, and/or can perform any other control command that causes a room, zone, and/or building to be controlled to a particular temperature).
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose …generating, by the device and using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters; and transmitting, by the device, information associated with identifying the one or more recommendations.
However, Kumar discloses …generating, by the device and using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters; and transmitting, by the device, information associated with identifying the one or more recommendations (Kumar, ¶ 10, The system of the present invention can also include a computing layer for storing the data from the plurality of data sources prior to storing the data in the data layer. The digital trust infrastructure unit employs a blockchain for storing any combination of the environmental data, the enriched environmental data, the financial data, and one or more of the pre-defined techniques. Further, the cognitive intelligence unit can further include a recommendation engine (discloses recommendation engine) for applying a machine learning technique to the environmental data from the data layer to generate predictions based on the environmental data. The cognitive intelligence unit can further include a risk model unit for applying one or more risk modelling techniques to the environmental data from the data layer. The risk model unit and the recommendation engine consume the enriched environmental data that is stored in the digital trust infrastructure unit, and the digital trust infrastructure unit employs a blockchain for storing the enriched environmental data), (Id., ¶ 43, The data collection and processing system 10 of the present invention can be employed to create an environmental and financial infrastructure for allowing financial institutions to process environmental and/or financial data so as to provide financial, tax, accounting, consulting, and business related services (including risk modelling services) to clients. By way of example, and for purposes of simplicity, FIG. 3 shows an example of the data collection and processing system 10 configured for processing environmental data according to an exemplary technique. Those of ordinary skill in the art will readily recognize that the system 10 can be employed to gather, process and analyze many different types of data. The data can be provided by data sources 12 that provide environmental data associated with a structure, such as a building 54. In the illustrated embodiment, the data sources 12 can provide original or raw environmental data associated with various building associated parameters, such as for example, energy consumption, humidity, occupancy, air quality, water usage, and the like. The original environmental data can be communicated via the network 14 to the computing layer 18 and then to the data analysis module 16. Specifically, the original environmental data can be collected in the computing layer 18 and can be indicative of asset grade environmental data. An example of a software application that can be employed to ingest or collect the environmental data in the computing layer 18 is any of the suitable software application from Context Labs. The original environmental data stored in the computing layer 18 is then transferred or conveyed to the enrichment unit 22 of the data analysis module 16. The enrichment layer 22 can include for example the application interface unit 32 and the cognitive intelligence unit 34. In the application interface unit 32, suitable software, such as Nantum OS from Prescriptive Data, can store and analyze the environmental data received from the data sources 12 associated with the building 54. The enrichment layer 22 can recommend adjustments to be made to the building operational systems so as to improve overall building efficiency and tenant comfort. The application interface unit 32 can also include the financial subsystem 37, FIG. 2, for applying financial concepts and logic to the environmental data and for generating or extracting financial data therefrom. Further, environmental data from the third party sources 38 can also be provided to further enrich the original environmental data and optionally the financial data. The third party data 38 can include for example environmental data associated with the operations of the building 54 or similar or different types of buildings, specifications of equipment employed in the building 54, external environmental data such as geospatial, weather, temperature, humidity, wind, sun exposure and the like, spatial information regarding the building layout, power grid information, and maintenance information regarding one or more aspects of the physical plant. This list of third party data is merely exemplary and is not intended to be exhaustive. The third party data 38 is intended to improve the quality and integrity of the environmental data. The cognitive intelligence unit 34 can include techniques or models for synthesizing, improving or optimizing the original environmental data, including data associated with overall consumption and measuring of the overall consumption, and then subsequently analyzing the data using risk modelling, machine learning or artificial intelligence techniques. The cognitive intelligence unit 34 can derive insights relative to the environmental data for decision making, performance, optimization and risk management. The processed and analyzed (e.g., enriched) data in the enrichment unit 22 can then be conveyed back to the building 54 to adjust or control on or more building environmental or operational parameters and/or can be conveyed to the digital trust infrastructure unit 20. When conveyed back to the building 54, the various systems in the building can be modified so as to operate the physical facility in a more cost and environmentally friendly and efficient manner).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the recommendation engine elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure.
The motivation for doing so would have been to “improve overall building efficiency and tenant comfort” [Kumar, ¶ 43], wherein such improvements would benefit Papadopoulos’ method which seeks to improve “efficiency control algorithms that result in reduced electrical energy consumption, reduced amount of electrical energy consumption due to installation of higher efficiency equipment, reduced amount of electrical energy consumption due to incentive programs for occupants to use less energy, [and] reduced amount of electrical energy consumption due to installation of higher efficiency energy consuming devices such as lighting devices, etc…” [Kumar, ¶ 43; Papadopoulos, ¶ 178].
Regarding Claim 2, the combination of Papadopoulos and Kumar discloses …the method of claim 1…
Papadopoulos further discloses …wherein generating the digital twin comprises: modeling one or more entities associated with at least one of: a service characteristic, a project characteristic, a release characteristic, a code characteristic, or an energy characteristic (Papadopoulos, ¶ 222, In some embodiments, the virtual environment server 2332 can generate a virtual environment (e.g., a digital twin) (discloses digital twin) for the building 10 and/or for the building model that pertains to building 10. For example, the virtual environment server 2332 can receive, from the communications interface 2302, the building model 2322. The virtual environment server 2332 can, using the building model 2322, generate the virtual environment for the building 10. The virtual environment server 2332 can include at least electronic device 2334. The electronic device 2334 can be or include at least one of a Head-Mounted Display (HMD), a pair of smart glasses, a mobile device, a virtual reality (VR) headset and/or any other computing device that a user can use to view or interact with the Metaverse), (Id., ¶ 231, Process 2400 includes receiving certification information for one or more building components of a building model (step 2402), according to some embodiments. In some embodiments, the information receiver 2310 can receive certification information for one building components. For example, the information receiver 2310 can receive the certification information from the database 2320. The database 2320 can provide, to the information receiver 2310, at least one of the building model 2322, the building energy data 2324 (discloses energy characteristics) and/or the certifications 2326. The certification information can be or include at least one of requirements, standards, milestones, regulations or any other possible guideline that results in reaching or achieving a certification. The information receiver 2310 can provide, to the energy savings identifier 2312, the information received from the database 2320).
Regarding Claim 3, the combination of Papadopoulos and Kumar discloses …the method of claim 1…
Papadopoulos further discloses …wherein generating the digital twin comprises: identifying a set of energy providers for the computing system and a set of carbon intensity estimates associated with the set of energy providers (Papadopoulos, ¶ 222, In some embodiments, the virtual environment server 2332 can generate a virtual environment (e.g., a digital twin) (discloses digital twin) for the building 10 and/or for the building model that pertains to building 10. For example, the virtual environment server 2332 can receive, from the communications interface 2302, the building model 2322. The virtual environment server 2332 can, using the building model 2322, generate the virtual environment for the building 10. The virtual environment server 2332 can include at least electronic device 2334. The electronic device 2334 can be or include at least one of a Head-Mounted Display (HMD), a pair of smart glasses, a mobile device, a virtual reality (VR) headset and/or any other computing device that a user can use to view or interact with the Metaverse), (Id., ¶ 178, The BAS controller 1902 can also obtain utility data 1914 (e.g., from a utility provider, from an energy provider, (discloses energy providers) etc.). In some embodiments, the utility data 1914 includes historical metering data, historical electrical energy consumption of a particular customer or building, historical natural gas consumption of a particular customer or building, a monetary cost per unit of electrical energy or natural gas, a carbon emissions cost per unit of electrical energy (discloses carbon intensity estimates) or natural gas, etc. In some embodiments, the utility data includes an amount of electrical energy that is consumed by a building (e.g., the building 10), multiple buildings 10 of a campus, a total energy consumption of a campus of buildings 10, energy consumption of a particular piece or unit of building equipment (e.g., chillers, VRF units, boilers, etc.), or electrical energy consumption of any other building, equipment, collection of spaces or equipment, collections of buildings, etc. In some embodiments, the BAS controller 1902 is also configured to obtain data from one or more electrical meters that are configured to measure metered amounts of electrical energy, natural gas consumption, etc., of any building, collection of buildings, equipment, collection of equipment, etc. In some embodiments, the BAS controller 1902 is configured to generate a baseline for the building 10 (e.g., baseline or expected energy consumption) using the utility data 1914 so that subsequent changes in electrical energy consumption (e.g., reduction in purchased electricity from a power grid, improved efficiency control algorithms that result in reduced electrical energy consumption, reduced amount of electrical energy consumption due to installation of higher efficiency equipment, reduced amount of electrical energy consumption due to incentive programs for occupants to use less energy, reduced amount of electrical energy consumption due to installation of higher efficiency energy consuming devices such as lighting devices, etc.) can be compared to the baseline to identify a corresponding reduction in carbon emissions).
Regarding Claim 4, the combination of Papadopoulos and Kumar discloses …the method of claim 1…
Papadopoulos further discloses …wherein determining the set of energy consumption metrics comprises: determining a set of emissions metrics associated with the computing system (Papadopoulos, ¶ 86, HVAC devices 1-6 can monitor energy consumption, (discloses energy consumption metrics) control lighting systems, control HVAC systems, or perform other functions within the BMS. HVAC devices 1-6 can be configured to optimize the systems they control based on energy efficiency, occupants comfort and productivity, and corporate or regulatory policies. HVAC devices 1-6 may be various HVAC devices, building lighting devices, building security devices (e.g., cameras, access control, etc.), safety devices (e.g., devices of a fire system), and/or devices of any other system of a building (e.g., building 10). In some embodiments, using temperature information from a room and/or zone as well as temperature data from an outside sensor, HVAC devices 1-6 can be configured to affect environmental changes in the room and/or zone. In some embodiments, HVAC devices 1-6 can cause dampers (e.g., actuators 526a-c) located in an air duct to be opened or closed, can activate heating, cooling, and/or air circulation equipment in the building and/or zone, and/or can perform any other control command that causes a room, zone, and/or building to be controlled to a particular temperature), (Id., ¶ 88, HVAC devices 1-6 are shown to each include HVAC data chain 510. HVAC data chain 510 can be a chain of one or more data blocks where each data block is linked to a previous block, thus, forming a chain. The chain may be a chain of sequentially ordered blocks. In some embodiments, HVAC data chain 510 is a blockchain database. HVAC devices 1-6 can exchange building data that they collect via controllers 522a-d, sensors 524a-c, and/or actuators 526a-c via HVAC data chain 510. In some embodiments, HVAC data chain 510 includes information regarding energy consumption, load curtailment, greenhouse gas emissions, (discloses emissions metrics) carbon credit entitlement or carbon credit balances, or other information that can be used to validate or transact carbon credits for HVAC devices 1-6, access to data for HVAC devices 1-6, and various other building related information. HVAC data chain 510 can be the same on each of HVAC devices 1-6. This allows each device of system 500 to have access to the same information. Further, this allows each of HVAC devices 1-6 to communicate with each other directly rather than relying on a central computing system).
Regarding Claim 5, the combination of Papadopoulos and Kumar discloses …the method of claim 4…
Papadopoulos further discloses …wherein determining the set of emissions metrics comprises: determining a software carbon intensity associated with a computing task performable by the computing system (Id., ¶ 83, In some embodiments, HVAC devices 1-6 can be computing devices that utilize Software Agents. Software Agents are described with further reference to U.S. patent application Ser. No. 15/367,167 filed Dec. 1, 2016, the entirety of which is incorporated by reference herein (discloses software entities)), (Id., ¶ 178, The BAS controller 1902 can also obtain utility data 1914 (e.g., from a utility provider, from an energy provider, (discloses energy providers) etc.). In some embodiments, the utility data 1914 includes historical metering data, historical electrical energy consumption of a particular customer or building, historical natural gas consumption of a particular customer or building, a monetary cost per unit of electrical energy or natural gas, a carbon emissions cost per unit of electrical energy (discloses carbon intensity estimates) or natural gas, etc. In some embodiments, the utility data includes an amount of electrical energy that is consumed by a building (e.g., the building 10), multiple buildings 10 of a campus, a total energy consumption of a campus of buildings 10, energy consumption of a particular piece or unit of building equipment (e.g., chillers, VRF units, boilers, etc.), or electrical energy consumption of any other building, equipment, collection of spaces or equipment, collections of buildings, etc. In some embodiments, the BAS controller 1902 is also configured to obtain data from one or more electrical meters that are configured to measure metered amounts of electrical energy, natural gas consumption, etc., of any building, collection of buildings, equipment, collection of equipment, etc. In some embodiments, the BAS controller 1902 is configured to generate a baseline for the building 10 (e.g., baseline or expected energy consumption) using the utility data 1914 so that subsequent changes in electrical energy consumption (e.g., reduction in purchased electricity from a power grid, improved efficiency control algorithms that result in reduced electrical energy consumption, reduced amount of electrical energy consumption due to installation of higher efficiency equipment, reduced amount of electrical energy consumption due to incentive programs for occupants to use less energy, reduced amount of electrical energy consumption due to installation of higher efficiency energy consuming devices such as lighting devices, etc.) can be compared to the baseline to identify a corresponding reduction in carbon emissions).
Regarding Claim 6, the combination of Papadopoulos and Kumar discloses …the method of claim 4…
Papadopoulos further discloses …wherein determining the set of emissions metrics comprises: generating a set of benchmarking scores for the set of emissions metrics, a benchmarking score, of the set of benchmarking scores, identifying a relative position of a corresponding emission metric, of the set of emissions metrics, in a range of candidate values for the corresponding emission metric (Id., ¶ 88, HVAC devices 1-6 are shown to each include HVAC data chain 510. HVAC data chain 510 can be a chain of one or more data blocks where each data block is linked to a previous block, thus, forming a chain. The chain may be a chain of sequentially ordered blocks. In some embodiments, HVAC data chain 510 is a blockchain database. HVAC devices 1-6 can exchange building data that they collect via controllers 522a-d, sensors 524a-c, and/or actuators 526a-c via HVAC data chain 510. In some embodiments, HVAC data chain 510 includes information regarding energy consumption, load curtailment, greenhouse gas emissions, (discloses emissions metrics) carbon credit entitlement or carbon credit balances, or other information that can be used to validate or transact carbon credits for HVAC devices 1-6, access to data for HVAC devices 1-6, and various other building related information. HVAC data chain 510 can be the same on each of HVAC devices 1-6. This allows each device of system 500 to have access to the same information. Further, this allows each of HVAC devices 1-6 to communicate with each other directly rather than relying on a central computing system), (Id., ¶ 178, The BAS controller 1902 can also obtain utility data 1914 (e.g., from a utility provider, from an energy provider, etc.). In some embodiments, the utility data 1914 includes historical metering data, historical electrical energy consumption of a particular customer or building, historical natural gas consumption of a particular customer or building, a monetary cost per unit of electrical energy or natural gas, a carbon emissions cost per unit of electrical energy or natural gas, etc. In some embodiments, the utility data includes an amount of electrical energy that is consumed by a building (e.g., the building 10), multiple buildings 10 of a campus, a total energy consumption of a campus of buildings 10, energy consumption of a particular piece or unit of building equipment (e.g., chillers, VRF units, boilers, etc.), or electrical energy consumption of any other building, equipment, collection of spaces or equipment, collections of buildings, etc. In some embodiments, the BAS controller 1902 is also configured to obtain data from one or more electrical meters that are configured to measure metered amounts of electrical energy, natural gas consumption, etc., of any building, collection of buildings, equipment, collection of equipment, etc. In some embodiments, the BAS controller 1902 is configured to generate a baseline for the building 10 (e.g., baseline or expected energy consumption) (discloses benchmark score) using the utility data 1914 so that subsequent changes in electrical energy consumption (e.g., reduction in purchased electricity from a power grid, improved efficiency control algorithms that result in reduced electrical energy consumption, reduced amount of electrical energy consumption due to installation of higher efficiency equipment, reduced amount of electrical energy consumption due to incentive programs for occupants to use less energy, reduced amount of electrical energy consumption due to installation of higher efficiency energy consuming devices such as lighting devices, etc.) can be compared to the baseline to identify a corresponding reduction in carbon emissions), (Id., ¶ 191, Referring to FIGS. 19 and 20, the MV application 1922 may provide measurement and verification data to the carbon offset token determination manager 1952 which may use the measurement and verification data to determine the carbon offset tokens. In some embodiments, the CUP application 1920 is configured to provide energy savings data that is determined based on facility improvement measures (FIMs) such as upgrades of equipment or HVAC sub-systems or system, repairing faults of building HVAC equipment, etc. In some embodiments, the carbon offset token determination manager 1952 is configured to use the energy savings data (e.g., relative to a baseline energy consumption of the building 10) (discloses emission metrics relative to a baseline) in order to determine the carbon offset tokens (e.g., to determine the carbon emissions reductions due to the energy savings). As described herein, the carbon offset determinations as performed by the offset determination manager 1938 or the offset value estimator 1956 can be performed by comparing energy usage of the building 10, or components of the building 10 (e.g., building equipment) to offset thresholds (e.g., a required threshold or amount of carbon offset required in order to obtain a carbon offset token) in order to determine the carbon offsets. In some embodiments, the carbon offset determinations are performed based on the data from the metering devices 1912, the energy generation of various green energy sources, equipment data, demand reduction, the measurement and verification data provided by the MV application 1922, demand response of the building 10 or a campus of buildings 10 relative to a baseline).
Regarding Claim 7, the combination of Papadopoulos and Kumar discloses …the method of claim 1…
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose …wherein generating the one or more recommendations comprises: identifying, based on the set of energy consumption metrics for the set of candidate parameters, a best energy consumption metric associated with a best candidate parameter; and wherein transmitting the information associated with identifying the one or more recommendations comprises: transmitting information identifying the best candidate parameter, the one or more recommendations being related to implementing the best candidate parameter.
However, Kumar discloses …wherein generating the one or more recommendations comprises: identifying, based on the set of energy consumption metrics for the set of candidate parameters, a best energy consumption metric associated with a best candidate parameter; and wherein transmitting the information associated with identifying the one or more recommendations comprises: transmitting information identifying the best candidate parameter, the one or more recommendations being related to implementing the best candidate parameter (Kumar, ¶ 43, The data collection and processing system 10 of the present invention can be employed to create an environmental and financial infrastructure for allowing financial institutions to process environmental and/or financial data so as to provide financial, tax, accounting, consulting, and business related services (including risk modelling services) to clients. By way of example, and for purposes of simplicity, FIG. 3 shows an example of the data collection and processing system 10 configured for processing environmental data according to an exemplary technique. Those of ordinary skill in the art will readily recognize that the system 10 can be employed to gather, process and analyze many different types of data. The data can be provided by data sources 12 that provide environmental data associated with a structure, such as a building 54. In the illustrated embodiment, the data sources 12 can provide original or raw environmental data associated with various building associated parameters, such as for example, energy consumption, humidity, occupancy, air quality, water usage, and the like. (discloses candidate parameters) The original environmental data can be communicated via the network 14 to the computing layer 18 and then to the data analysis module 16. Specifically, the original environmental data can be collected in the computing layer 18 and can be indicative of asset grade environmental data. An example of a software application that can be employed to ingest or collect the environmental data in the computing layer 18 is any of the suitable software application from Context Labs. The original environmental data stored in the computing layer 18 is then transferred or conveyed to the enrichment unit 22 of the data analysis module 16. The enrichment layer 22 can include for example the application interface unit 32 and the cognitive intelligence unit 34. In the application interface unit 32, suitable software, such as Nantum OS from Prescriptive Data, can store and analyze the environmental data received from the data sources 12 associated with the building 54. The enrichment layer 22 can recommend adjustments to be made to the building operational systems so as to improve overall building efficiency and tenant comfort. (discloses recommendation) The application interface unit 32 can also include the financial subsystem 37, FIG. 2, for applying financial concepts and logic to the environmental data and for generating or extracting financial data therefrom. Further, environmental data from the third party sources 38 can also be provided to further enrich the original environmental data and optionally the financial data. The third party data 38 can include for example environmental data associated with the operations of the building 54 or similar or different types of buildings, specifications of equipment employed in the building 54, external environmental data such as geospatial, weather, temperature, humidity, wind, sun exposure and the like, spatial information regarding the building layout, power grid information, and maintenance information regarding one or more aspects of the physical plant. This list of third party data is merely exemplary and is not intended to be exhaustive. The third party data 38 is intended to improve the quality and integrity of the environmental data. The cognitive intelligence unit 34 can include techniques or models for synthesizing, improving or optimizing the original environmental data, including data associated with overall consumption and measuring of the overall consumption, and then subsequently analyzing the data using risk modelling, machine learning or artificial intelligence techniques. The cognitive intelligence unit 34 can derive insights relative to the environmental data for decision making, performance, optimization and risk management. The processed and analyzed (e.g., enriched) data in the enrichment unit 22 can then be conveyed back to the building 54 to adjust or control on or more building environmental or operational parameters and/or can be conveyed to the digital trust infrastructure unit 20. When conveyed back to the building 54, the various systems in the building can be modified so as to operate the physical facility in a more cost and environmentally friendly and efficient manner), (Id., ¶ 46, The data stored in the blocks of the blockchain 20A can be retrieved and processed by the post-processing unit 24 to generate one or more reports, such as environmental or financial reports, or other types of reports, via one or more suitable report generation applications. The environmental data can be employed to identify and analyze the building units that are generating emissions and how best to reduce the emissions (discloses identifying a best energy consumption metric) and how the building is being powered. From this environmental data, the data analysis module 16 can determine the operational health of the building and associated systems, and perform a risk analysis on the physical building and associated external and internal climate. The reports can include among other things reports on green house gas emissions, green house gas optimization, carbon emission setting and optimization, risks and associated controls, transaction settlements, net-zero emissions compliance, and carbon pricing. The post-processing unit 24 can also optionally create if desired a reporting dashboard. The reports and the dashboard can be displayed in the display region 50, FIG. 2. The enrichment unit 22 can also include an optional financial analysis unit 74, similar to the financial subsystem 37, that can reside for example as part of the cognitive intelligence unit 34 or as a separate component or unit. The financial analysis unit 74 can analyze the data received from the digital trust infrastructure unit 20 to identify, process and analyze certain financial aspects of the environmental data. For example, the data can be processed using standard financial or accounting rules, logic and models and techniques. Also the financial analysis unit 74 can determine the carbon footprint associated with the environmental data. The financial analysis unit 74 can be used in place of the financial subsystem 37 or can be used in conjunction with the financial subsystem 37).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the recommendation engine elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure for the same reasons as stated for claim 1.
Regarding Claim 8, Papadopoulos discloses …A device for wireless communication, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: receive information identifying a computing system for energy management, the computing system having a set of hardware components associated with first energy information, a set of virtual machines associated with second energy information, and a set of software entities associated with third energy information (Papadopoulos, ¶ 4, One implementation of the present disclosure is a building automation system (BAS) for a building, according to some embodiments. In some embodiments, the BAS includes multiple devices including processing circuitry. The processing circuitry is configured to obtain time series data indicating reduced energy consumption of building equipment of the building, or green energy generation for the building, according to some embodiments. The processing circuitry is also configured to determine a corresponding carbon emissions reduction resulting from the reduced energy consumption of the building equipment or the green energy generation, according to some embodiments. The processing circuitry is also configured to create a carbon offset token responsive to the corresponding carbon emissions reduction being greater than a threshold, according to some embodiments. The processing circuitry is also configured to validate a new block of a first blockchain, according to some embodiments. In some embodiments, the carbon offset token is an attribute or data of the new block of the first blockchain, the first blockchain being limited from public access. In some embodiments, the processing circuitry is configured to provide the first blockchain as an input to a new block or a sidechain of a second blockchain. In some embodiments, the second blockchain is publicly accessible and includes the carbon offset token as a result of providing the first blockchain as an input to the new block or sidechain of the second blockchain), (Id., ¶ 111, HVAC device 1 is shown to communicate via network 508 and network 902 via network interface 912. Network interface 912 can be configured to communicate with network 508 and/or network 902. Network interface 912 can be one or more circuits configured to allow HVAC device 1 to communicate with network 508 and/or network 902. Network interface 912 can be configured to communicate via LANs, metropolitan area networks (MANs), and WANs. Network interface 912 can be configured to communicate via the Internet, Ethernet, Wi-Fi, a field bus, MODBUS, BACnet, CAN, near field communication (NFC), Zigbee, Bluetooth, and/or any other network and/or combination thereof. Network interface 912 can include one or more wired and/or wireless transceivers and/or receives (e.g., a Wi-Fi transceiver, a Bluetooth transceiver, a NFC transceiver, a cellular transceiver, etc.)), (Id., ¶ 53, Airside system 130 can deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and can provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) (discloses hardware components associated with first energy information) configured to measure attributes of the supply airflow. AHU 106 can receive input from sensors located within AHU 106 and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone), (Id., ¶ 239, Process 2500 includes obtaining equipment information associated with one or more physical or non-physical building equipment (step 2502), according to some embodiments. The communications interface 2302 can obtain equipment information. The equipment information can include ownership data and technical data. The equipment information can be associated with physical and/or non-physical building equipment. Physical building equipment can include, for example, hardware-based devices having a housing, circuitry, processors, memory, power supplies, and/or other physical hardware (h/w) components. Non-physical building equipment can include, for example, software-based devices such as virtual devices, digital twins, virtual machines, (discloses virtual machines) emulated equipment, device software (s/w) or other equipment that exists in a virtual or non-physical form. The communications interface 2302 can obtain the equipment information from the building agencies server 2330. The communications interface 2302 can provide the equipment information to the NFT generator 2318), (Id., ¶ 48, Using blockchain with HVAC devices can result in higher levels of security for a building management system (BMS) since a single point of failure is removed. If a cyber-attack is launched against the BMS, the cyber-attack would need to compromise the HVAC data chain of all the HVAC devices instead of just the data stored on a central server of the BMS. Using the HVAC data chain can result in a high byzantine fault tolerance. Further, there may be mass disintermediation due to consensus-driven decision-making associated with the HVAC data chain. Further, using the HVAC data chain may lower data corruption security breach uncertainties. Using blockchain can allow the HVAC devices to enter permanent and/or time based contracts or transactions among each other without any human intervention. In a building with HVAC devices, the HVAC devices can use blockchain to store and validate energy consumption information, load curtailment information, carbon credits, (discloses second and third energy information) transmit access rights and licensing information, transmit control actions that can have significant impact on the controlled environment, perform transaction negotiation between two HVAC devices, transport sensitive information across the HVAC device network, and can be used for any communication in building automation and control implementations that have environment requirements that require validation and security. The HVAC data chain can be used for any communication in a BMS (e.g., transport of sensitive information) and/or other control implementation that have validated environment requirements), (Id., ¶ 82, Referring now to FIG. 5, a system 500 of HVAC devices communicating via a network, with the HVAC devices storing an HVAC data chain 510, is shown, according to some embodiments. System 500 is shown to include a number of HVAC devices communicating via a network 508 (e.g., a distributed network). The number of HVAC devices are shown to include HVAC device 1, HVAC device 2, HVAC device 3, HVAC device 4, HVAC device 5, and HVAC device 6. There can be any number of HVAC devices in system 500. HVAC devices 1-6 can be any controller, actuator, or sensor that can communicate via network 508. In some embodiments, HVAC devices 1-6 are building gateways and/or various other building devices or building controllers. HVAC devices 1-6 can be the devices of smart connected things 204 and/or gateway 206 as described with reference to FIGS. 2-3 (e.g., controllers 226a-b, actuators 224, sensors 220, etc.) In some embodiments, HVAC devices 1-6 are VAV boxes of VAV units 116, AHU 106, chiller 102, and/or boiler 104 as described with reference to FIG. 1), (Id., ¶ 83, In some embodiments, HVAC devices 1-6 can be computing devices that utilize Software Agents. Software Agents are described with further reference to U.S. patent application Ser. No. 15/367,167 filed Dec. 1, 2016, the entirety of which is incorporated by reference herein (discloses software entities));
generate a digital twin of the computing system using the first energy information for simulation of the set of hardware components, the second energy information for simulation of the set of virtual machines, and the third energy information for simulation of the set of software entities (Id., ¶ 222, In some embodiments, the virtual environment server 2332 can generate a virtual environment (e.g., a digital twin) (discloses digital twin) for the building 10 and/or for the building model that pertains to building 10. For example, the virtual environment server 2332 can receive, from the communications interface 2302, the building model 2322. The virtual environment server 2332 can, using the building model 2322, generate the virtual environment for the building 10. The virtual environment server 2332 can include at least electronic device 2334. The electronic device 2334 can be or include at least one of a Head-Mounted Display (HMD), a pair of smart glasses, a mobile device, a virtual reality (VR) headset and/or any other computing device that a user can use to view or interact with the Metaverse), (Id., ¶ 82, Referring now to FIG. 5, a system 500 of HVAC devices communicating via a network, with the HVAC devices storing an HVAC data chain 510, is shown, according to some embodiments. System 500 is shown to include a number of HVAC devices communicating via a network 508 (e.g., a distributed network). The number of HVAC devices are shown to include HVAC device 1, HVAC device 2, HVAC device 3, HVAC device 4, HVAC device 5, and HVAC device 6. There can be any number of HVAC devices in system 500. HVAC devices 1-6 can be any controller, actuator, or sensor that can communicate via network 508. In some embodiments, HVAC devices 1-6 are building gateways and/or various other building devices or building controllers. HVAC devices 1-6 can be the devices of smart connected things 204 and/or gateway 206 as described with reference to FIGS. 2-3 (e.g., controllers 226a-b, actuators 224, sensors 220, etc.) In some embodiments, HVAC devices 1-6 are VAV boxes of VAV units 116, AHU 106, chiller 102, and/or boiler 104 as described with reference to FIG. 1), (Id., ¶ 53, Airside system 130 can deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and can provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) (discloses hardware components associated with first energy information) configured to measure attributes of the supply airflow. AHU 106 can receive input from sensors located within AHU 106 and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone), (Id., ¶ 53, Airside system 130 can deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and can provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) (discloses hardware components associated with first energy information) configured to measure attributes of the supply airflow. AHU 106 can receive input from sensors located within AHU 106 and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone), (Id., ¶ 239, Process 2500 includes obtaining equipment information associated with one or more physical or non-physical building equipment (step 2502), according to some embodiments. The communications interface 2302 can obtain equipment information. The equipment information can include ownership data and technical data. The equipment information can be associated with physical and/or non-physical building equipment. Physical building equipment can include, for example, hardware-based devices having a housing, circuitry, processors, memory, power supplies, and/or other physical hardware (h/w) components. Non-physical building equipment can include, for example, software-based devices such as virtual devices, digital twins, virtual machines, (discloses virtual machines) emulated equipment, device software (s/w) or other equipment that exists in a virtual or non-physical form. The communications interface 2302 can obtain the equipment information from the building agencies server 2330. The communications interface 2302 can provide the equipment information to the NFT generator 2318), (Id., ¶ 48, Using blockchain with HVAC devices can result in higher levels of security for a building management system (BMS) since a single point of failure is removed. If a cyber-attack is launched against the BMS, the cyber-attack would need to compromise the HVAC data chain of all the HVAC devices instead of just the data stored on a central server of the BMS. Using the HVAC data chain can result in a high byzantine fault tolerance. Further, there may be mass disintermediation due to consensus-driven decision-making associated with the HVAC data chain. Further, using the HVAC data chain may lower data corruption security breach uncertainties. Using blockchain can allow the HVAC devices to enter permanent and/or time based contracts or transactions among each other without any human intervention. In a building with HVAC devices, the HVAC devices can use blockchain to store and validate energy consumption information, load curtailment information, carbon credits, (discloses second and third energy information) transmit access rights and licensing information, transmit control actions that can have significant impact on the controlled environment, perform transaction negotiation between two HVAC devices, transport sensitive information across the HVAC device network, and can be used for any communication in building automation and control implementations that have environment requirements that require validation and security. The HVAC data chain can be used for any communication in a BMS (e.g., transport of sensitive information) and/or other control implementation that have validated environment requirements), (Id., ¶ 83, In some embodiments, HVAC devices 1-6 can be computing devices that utilize Software Agents. Software Agents are described with further reference to U.S. patent application Ser. No. 15/367,167 filed Dec. 1, 2016, the entirety of which is incorporated by reference herein (discloses software entities));
determine, using the digital twin of the computing system, a set of energy consumption metrics, for the computing system, associated with a set of candidate parameters (Id., ¶ 222, In some embodiments, the virtual environment server 2332 can generate a virtual environment (e.g., a digital twin) (discloses digital twin) for the building 10 and/or for the building model that pertains to building 10. For example, the virtual environment server 2332 can receive, from the communications interface 2302, the building model 2322. The virtual environment server 2332 can, using the building model 2322, generate the virtual environment for the building 10. The virtual environment server 2332 can include at least electronic device 2334. The electronic device 2334 can be or include at least one of a Head-Mounted Display (HMD), a pair of smart glasses, a mobile device, a virtual reality (VR) headset and/or any other computing device that a user can use to view or interact with the Metaverse), (Id., ¶ 86, HVAC devices 1-6 can monitor energy consumption, (discloses energy consumption metrics within HVAC device parameters) control lighting systems, control HVAC systems, or perform other functions within the BMS. HVAC devices 1-6 can be configured to optimize the systems they control based on energy efficiency, occupants comfort and productivity, and corporate or regulatory policies. HVAC devices 1-6 may be various HVAC devices, building lighting devices, building security devices (e.g., cameras, access control, etc.), safety devices (e.g., devices of a fire system), and/or devices of any other system of a building (e.g., building 10). In some embodiments, using temperature information from a room and/or zone as well as temperature data from an outside sensor, HVAC devices 1-6 can be configured to affect environmental changes in the room and/or zone. In some embodiments, HVAC devices 1-6 can cause dampers (e.g., actuators 526a-c) located in an air duct to be opened or closed, can activate heating, cooling, and/or air circulation equipment in the building and/or zone, and/or can perform any other control command that causes a room, zone, and/or building to be controlled to a particular temperature).
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose … generate, using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters; and transmit information associated with implementing the one or more recommendations.
However, Kumar discloses … generate, using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters; and transmit information associated with implementing the one or more recommendations (Kumar, ¶ 10, The system of the present invention can also include a computing layer for storing the data from the plurality of data sources prior to storing the data in the data layer. The digital trust infrastructure unit employs a blockchain for storing any combination of the environmental data, the enriched environmental data, the financial data, and one or more of the pre-defined techniques. Further, the cognitive intelligence unit can further include a recommendation engine (discloses recommendation engine) for applying a machine learning technique to the environmental data from the data layer to generate predictions based on the environmental data. The cognitive intelligence unit can further include a risk model unit for applying one or more risk modelling techniques to the environmental data from the data layer. The risk model unit and the recommendation engine consume the enriched environmental data that is stored in the digital trust infrastructure unit, and the digital trust infrastructure unit employs a blockchain for storing the enriched environmental data), (Id., ¶ 43, The data collection and processing system 10 of the present invention can be employed to create an environmental and financial infrastructure for allowing financial institutions to process environmental and/or financial data so as to provide financial, tax, accounting, consulting, and business related services (including risk modelling services) to clients. By way of example, and for purposes of simplicity, FIG. 3 shows an example of the data collection and processing system 10 configured for processing environmental data according to an exemplary technique. Those of ordinary skill in the art will readily recognize that the system 10 can be employed to gather, process and analyze many different types of data. The data can be provided by data sources 12 that provide environmental data associated with a structure, such as a building 54. In the illustrated embodiment, the data sources 12 can provide original or raw environmental data associated with various building associated parameters, such as for example, energy consumption, humidity, occupancy, air quality, water usage, and the like. The original environmental data can be communicated via the network 14 to the computing layer 18 and then to the data analysis module 16. Specifically, the original environmental data can be collected in the computing layer 18 and can be indicative of asset grade environmental data. An example of a software application that can be employed to ingest or collect the environmental data in the computing layer 18 is any of the suitable software application from Context Labs. The original environmental data stored in the computing layer 18 is then transferred or conveyed to the enrichment unit 22 of the data analysis module 16. The enrichment layer 22 can include for example the application interface unit 32 and the cognitive intelligence unit 34. In the application interface unit 32, suitable software, such as Nantum OS from Prescriptive Data, can store and analyze the environmental data received from the data sources 12 associated with the building 54. The enrichment layer 22 can recommend adjustments to be made to the building operational systems so as to improve overall building efficiency and tenant comfort. The application interface unit 32 can also include the financial subsystem 37, FIG. 2, for applying financial concepts and logic to the environmental data and for generating or extracting financial data therefrom. Further, environmental data from the third party sources 38 can also be provided to further enrich the original environmental data and optionally the financial data. The third party data 38 can include for example environmental data associated with the operations of the building 54 or similar or different types of buildings, specifications of equipment employed in the building 54, external environmental data such as geospatial, weather, temperature, humidity, wind, sun exposure and the like, spatial information regarding the building layout, power grid information, and maintenance information regarding one or more aspects of the physical plant. This list of third party data is merely exemplary and is not intended to be exhaustive. The third party data 38 is intended to improve the quality and integrity of the environmental data. The cognitive intelligence unit 34 can include techniques or models for synthesizing, improving or optimizing the original environmental data, including data associated with overall consumption and measuring of the overall consumption, and then subsequently analyzing the data using risk modelling, machine learning or artificial intelligence techniques. The cognitive intelligence unit 34 can derive insights relative to the environmental data for decision making, performance, optimization and risk management. The processed and analyzed (e.g., enriched) data in the enrichment unit 22 can then be conveyed back to the building 54 to adjust or control on or more building environmental or operational parameters and/or can be conveyed to the digital trust infrastructure unit 20. When conveyed back to the building 54, the various systems in the building can be modified so as to operate the physical facility in a more cost and environmentally friendly and efficient manner).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the recommendation engine elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure for the same reasons as stated for claim 1.
Regarding Claim 9, the combination of Papadopoulos and Kumar discloses …the device of claim 8…
Papadopoulos further discloses … identify a set of impacts of the set of candidate parameters (Papadopoulos, ¶ 48, Using blockchain with HVAC devices can result in higher levels of security for a building management system (BMS) since a single point of failure is removed. If a cyber-attack is launched against the BMS, the cyber-attack would need to compromise the HVAC data chain of all the HVAC devices instead of just the data stored on a central server of the BMS. Using the HVAC data chain can result in a high byzantine fault tolerance. Further, there may be mass disintermediation due to consensus-driven decision-making associated with the HVAC data chain. Further, using the HVAC data chain may lower data corruption security breach uncertainties. Using blockchain can allow the HVAC devices to enter permanent and/or time based contracts or transactions among each other without any human intervention. In a building with HVAC devices, the HVAC devices can use blockchain to store and validate energy consumption information, load curtailment information, carbon credits, transmit access rights and licensing information, transmit control actions that can have significant impact on the controlled environment, (discloses set of impacts on the environment parameters) perform transaction negotiation between two HVAC devices, transport sensitive information across the HVAC device network, and can be used for any communication in building automation and control implementations that have environment requirements that require validation and security. The HVAC data chain can be used for any communication in a BMS (e.g., transport of sensitive information) and/or other control implementation that have validated environment requirements.
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose … wherein the one or more processors, to generate the one or more recommendations, are configured to: …; and select a recommendation, from a set of available recommendations, to select a particular candidate parameter, of the set of candidate parameters, based on the set of impacts of the set of candidate parameters.
However, Kumar discloses … wherein the one or more processors, to generate the one or more recommendations, are configured to: …; and select a recommendation, from a set of available recommendations, to select a particular candidate parameter, of the set of candidate parameters, based on the set of impacts of the set of candidate parameters (Kumar, ¶ 52, The post-processing unit 24 can also include an optional risk management unit 88 that can include one or more software applications and associated hardware for processing the enriched environmental data to model or determine and hence manage any financial risk to the enterprise based on the environmental data. More specifically, the risk management unit 88 can determine the financial risk via a financial risk value or score associated with the enterprise based on the enriched environmental data or environmental data, evaluate the impact of various climate scenarios on the sustainable financial performance of the enterprise, evaluate supply chain and operations of the enterprise, and determine any existing and potential vulnerabilities in the systems available within the enterprise, based on the environmental data, and/or to determine whether the enterprise complies with selected regulations and to mitigate any deficiencies related thereto. The risk management unit 88 can also generate insights that can help the enterprise value any associated assets, such as buildings, facilities, infrastructures, and the like, and determine the underlying risks when underwriting financial instruments, such as getting loans at the right interest rate, deciding on investment strategy, reconfiguring the fund allocation actions, managing the brand equity or defining or employing risk mitigation strategies to lower the impact of climate events or scenarios on the financial performance of the enterprise), (Id., ¶ 57, The data collection and processing system 10 of the present invention integrates different technologies in a cloud based manner to collect environmental data from various data sources 12 and thus form the backbone of an environmental accounting infrastructure. The system 10 can process, enrich, audit and authenticate the environmental data in a highly automated manner. The cognitive intelligence unit 34 of the data analysis module 16 can derive granular insights and help enterprises make predictions for how best to optimize the building systems and operations, so as to help mitigate the overall environmental impact of the building. The reporting functions of the post-processing unit 24 and the financial verification unit 70 can allow the attestation body (e.g., accounting firm) to attest and certify environmental related disclosures for financial and non-financial reporting and compliance within the context of standard accounting frameworks), (Id., ¶ 10, The system of the present invention can also include a computing layer for storing the data from the plurality of data sources prior to storing the data in the data layer. The digital trust infrastructure unit employs a blockchain for storing any combination of the environmental data, the enriched environmental data, the financial data, and one or more of the pre-defined techniques. Further, the cognitive intelligence unit can further include a recommendation engine (discloses recommendation engine) for applying a machine learning technique to the environmental data from the data layer to generate predictions based on the environmental data. The cognitive intelligence unit can further include a risk model unit for applying one or more risk modelling techniques to the environmental data from the data layer. The risk model unit and the recommendation engine consume the enriched environmental data that is stored in the digital trust infrastructure unit, and the digital trust infrastructure unit employs a blockchain for storing the enriched environmental data).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the recommendation engine elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure for the same reasons as stated for claim 1.
Regarding Claim 10, the combination of Papadopoulos and Kumar discloses …the device of claim 8…
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose …wherein the one or more processors, to generate the one or more recommendations, are configured to: generate the one or more recommendations based at least in part on at least one of: an anomaly detection function, a state determination function, an impact analysis function, a root cause analysis function, a ranking engine function, or a resolution engine function.
However, Kumar discloses …wherein the one or more processors, to generate the one or more recommendations, are configured to: generate the one or more recommendations based at least in part on at least one of: an anomaly detection function, a state determination function, an impact analysis function, a root cause analysis function, a ranking engine function, or a resolution engine function (Kumar, ¶ 43, The data collection and processing system 10 of the present invention can be employed to create an environmental and financial infrastructure for allowing financial institutions to process environmental and/or financial data so as to provide financial, tax, accounting, consulting, and business related services (including risk modelling services) to clients. By way of example, and for purposes of simplicity, FIG. 3 shows an example of the data collection and processing system 10 configured for processing environmental data according to an exemplary technique. Those of ordinary skill in the art will readily recognize that the system 10 can be employed to gather, process and analyze many different types of data. The data can be provided by data sources 12 that provide environmental data associated with a structure, such as a building 54. In the illustrated embodiment, the data sources 12 can provide original or raw environmental data associated with various building associated parameters, such as for example, energy consumption, humidity, occupancy, air quality, water usage, and the like. The original environmental data can be communicated via the network 14 to the computing layer 18 and then to the data analysis module 16. Specifically, the original environmental data can be collected in the computing layer 18 and can be indicative of asset grade environmental data. An example of a software application that can be employed to ingest or collect the environmental data in the computing layer 18 is any of the suitable software application from Context Labs. The original environmental data stored in the computing layer 18 is then transferred or conveyed to the enrichment unit 22 of the data analysis module 16. The enrichment layer 22 can include for example the application interface unit 32 and the cognitive intelligence unit 34. In the application interface unit 32, suitable software, such as Nantum OS from Prescriptive Data, can store and analyze the environmental data received from the data sources 12 associated with the building 54. The enrichment layer 22 can recommend adjustments to be made to the building operational systems so as to improve overall building efficiency and tenant comfort. The application interface unit 32 can also include the financial subsystem 37, FIG. 2, for applying financial concepts and logic to the environmental data and for generating or extracting financial data therefrom. Further, environmental data from the third party sources 38 can also be provided to further enrich the original environmental data and optionally the financial data. The third party data 38 can include for example environmental data associated with the operations of the building 54 or similar or different types of buildings, specifications of equipment employed in the building 54, external environmental data such as geospatial, weather, temperature, humidity, wind, sun exposure and the like, spatial information regarding the building layout, power grid information, and maintenance information regarding one or more aspects of the physical plant. This list of third party data is merely exemplary and is not intended to be exhaustive. The third party data 38 is intended to improve the quality and integrity of the environmental data. The cognitive intelligence unit 34 can include techniques or models for synthesizing, improving or optimizing the original environmental data, including data associated with overall consumption and measuring of the overall consumption, and then subsequently analyzing the data using risk modelling, machine learning or artificial intelligence techniques. The cognitive intelligence unit 34 can derive insights relative to the environmental data for decision making, performance, optimization and risk management. The processed and analyzed (e.g., enriched) data in the enrichment unit 22 can then be conveyed back to the building 54 to adjust or control on or more building environmental or operational parameters and/or can be conveyed to the digital trust infrastructure unit 20. When conveyed back to the building 54, the various systems in the building can be modified so as to operate the physical facility in a more cost and environmentally friendly and efficient manner).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the recommendation engine elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure for the same reasons as stated for claim 1.
Regarding Claim 11, the combination of Papadopoulos and Kumar discloses …the device of claim 8…
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose … wherein the set of candidate parameters relate to a set of deployment sites;
and wherein the one or more processors, to generate the one or more recommendations, are configured to: select a deployment site, of the set of deployment sites, for the computing system based on the set of energy consumption metrics.
However, Kumar discloses … wherein the set of candidate parameters relate to a set of deployment sites (Kumar, ¶ 47, The post-processing unit 24 can also be configured to generate reports, which can include selected environmental data that is important to a selected client relative to one or more enterprises or buildings, (discloses deployment sites) and then display the reports to the system user. The reports can be constructed so as to allow the user to view and analyze the data, such as environmental data, so as to help make decisions or to take or recommend actions in response thereto and which are related to selected system capabilities or functionalities, including for example emissions accounting, emissions management, emissions reporting, emissions trading, and risk management. The data collection and processing system 10 of the present invention can employ selected software modules or units in the post-processing unit 24 directed to one or more of the foregoing capabilities. The selected software modules can be configured to process data from one or more selected types of data sources 12, thus allowing the client to access granular environmental data, such as for example emissions related data, so as to derive insights across the users operations in near real time)
and wherein the one or more processors, to generate the one or more recommendations, are configured to: select a deployment site, of the set of deployment sites, for the computing system based on the set of energy consumption metrics (Id., ¶ 48, FIG. 4 is a schematic block diagram showing the post-processing unit 24 configured to optionally include or employ one or more selected units for generating one or more reports directed to a selected system capability or functionality of the enterprise. For example, regarding the emission accounting capability, the post-processing unit 24 can be configured to include an optional emissions accounting unit 80 that can include one or more software applications and associated hardware for processing the enriched environmental data to aggregate data related to the energy consumed or used by the enterprise, such as a structure or collection of structures (e.g., buildings), equipment, facility, business, company, operation, organization, country or entity, to compute various emissions-related metrics using standard emissions factors available from third-party sources, for example, the emissions factors provided by the International Energy Agency (IEA); to track emissions relative to established emissions goals for a specific building, (discloses selecting a specific deployment site according to consumption metrics) equipment, enterprise, or country; and to determine the overall emissions related liabilities of the enterprise. Specifically, the emissions accounting unit 80 can determine or calculate the emissions of an enterprise or building, determine and track energy consumption of the enterprise, and/or determine or track the overall emissions goals for the enterprise based on the environmental data and/or third party data following various known climate-related accounting standards and frameworks. The climate related standards can include for example standards and frameworks established by greenhouse gas protocols, sustainability accounting standards boards (SASB), task forces on climate-related financial disclosures (TCFD), global logistics emissions council (GLEC), and global reporting institute (GRI). The emissions accounting unit 80 can also be configured to track emissions generated from their operations with the required level of granularity or specificity for deciding specific to reducing emissions through various known carbon management strategies. For example, the emissions accounting unit 80 can use the environmental data to determine the biggest emission generators in the building and then take selected emissions remediation actions (e.g., climate actions), such as for example upgrade the emission sources, initiate repair or maintenance of selected building components, and to forecast future investment needs. The climate actions can also include for example initiatives related to carbon reduction (e.g., energy efficiency), carbon removal, carbon offsetting, carbon capture and storage, and the like).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the site selection elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure for the same reasons as stated for claim 1.
Regarding Claim 12, the combination of Papadopoulos and Kumar discloses …the device of claim 8…
Papadopoulos further discloses …wherein the set of candidate parameters relate to a set of computing task; and wherein the one or more processors … are configured to: generate an assignment of a computing task, of the set of computing tasks, to the computing system based on the set of energy consumption metrics (Papadopoulos, ¶ 54, Referring now to FIG. 2, system 200 is shown to include devices of building 10 coupled to a cloud platform 202, according to an exemplary embodiment. Cloud platform 202 can be one or more controllers, servers, and/or any other computing device that can be located in building 10 and/or can be located remotely and/or connected to the systems of building 10 via networks (e.g., the Internet). Cloud platform 202 can be configured to cause building 10 and the devices of building 10 to be a “self-conscious building.” (discloses copmuting tasks) A self-conscious building can be a building in which building devices are interconnected via a cloud, e.g., cloud platform 202. A building of interconnected devices can be “self-conscious” in the regard that rather than having a specific controller make specific decisions for only a portion of the equipment in building 10, (discloses generating assignment of computing tasks) cloud platform 202 can receive and aggregate data from all devices of building 10 and thus make decisions for building 10 based on an aggregate set of data);
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose … to generate the one or more recommendations.
However, Kumar discloses … to generate the one or more recommendations (Kumar, ¶ 57, The data collection and processing system 10 of the present invention integrates different technologies in a cloud based manner to collect environmental data from various data sources 12 and thus form the backbone of an environmental accounting infrastructure. The system 10 can process, enrich, audit and authenticate the environmental data in a highly automated manner. The cognitive intelligence unit 34 of the data analysis module 16 can derive granular insights and help enterprises make predictions for how best to optimize the building systems and operations, so as to help mitigate the overall environmental impact of the building. The reporting functions of the post-processing unit 24 and the financial verification unit 70 can allow the attestation body (e.g., accounting firm) to attest and certify environmental related disclosures for financial and non-financial reporting and compliance within the context of standard accounting frameworks), (Id., ¶ 10, The system of the present invention can also include a computing layer for storing the data from the plurality of data sources prior to storing the data in the data layer. The digital trust infrastructure unit employs a blockchain for storing any combination of the environmental data, the enriched environmental data, the financial data, and one or more of the pre-defined techniques. Further, the cognitive intelligence unit can further include a recommendation engine (discloses recommendation engine) for applying a machine learning technique to the environmental data from the data layer to generate predictions based on the environmental data. The cognitive intelligence unit can further include a risk model unit for applying one or more risk modelling techniques to the environmental data from the data layer. The risk model unit and the recommendation engine consume the enriched environmental data that is stored in the digital trust infrastructure unit, and the digital trust infrastructure unit employs a blockchain for storing the enriched environmental data).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the recommendation engine elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure for the same reasons as stated for claim 1.
Regarding Claim 13, the combination of Papadopoulos and Kumar discloses …the device of claim 8…
Papadopoulos further discloses …wherein the set of candidate parameters relate to a set of possible configurations for the computing system; and wherein the one or more processors … are configured to: select a configuration, of the set of possible configurations, for the computing system (Papadopoulos, ¶ 54, Referring now to FIG. 2, system 200 is shown to include devices of building 10 coupled to a cloud platform 202, according to an exemplary embodiment. Cloud platform 202 can be one or more controllers, servers, and/or any other computing device that can be located in building 10 and/or can be located remotely and/or connected to the systems of building 10 via networks (e.g., the Internet). Cloud platform 202 can be configured to cause building 10 and the devices of building 10 to be a “self-conscious building.” (discloses copmuting tasks) A self-conscious building can be a building in which building devices are interconnected via a cloud, e.g., cloud platform 202. A building of interconnected devices can be “self-conscious” in the regard that rather than having a specific controller make specific decisions for only a portion of the equipment in building 10, (discloses generating assignment of computing tasks) cloud platform 202 can receive and aggregate data from all devices of building 10 and thus make decisions for building 10 based on an aggregate set of data);
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose … to generate the one or more recommendations.
However, Kumar discloses … to generate the one or more recommendations (Kumar, ¶ 57, The data collection and processing system 10 of the present invention integrates different technologies in a cloud based manner to collect environmental data from various data sources 12 and thus form the backbone of an environmental accounting infrastructure. The system 10 can process, enrich, audit and authenticate the environmental data in a highly automated manner. The cognitive intelligence unit 34 of the data analysis module 16 can derive granular insights and help enterprises make predictions for how best to optimize the building systems and operations, so as to help mitigate the overall environmental impact of the building. The reporting functions of the post-processing unit 24 and the financial verification unit 70 can allow the attestation body (e.g., accounting firm) to attest and certify environmental related disclosures for financial and non-financial reporting and compliance within the context of standard accounting frameworks), (Id., ¶ 10, The system of the present invention can also include a computing layer for storing the data from the plurality of data sources prior to storing the data in the data layer. The digital trust infrastructure unit employs a blockchain for storing any combination of the environmental data, the enriched environmental data, the financial data, and one or more of the pre-defined techniques. Further, the cognitive intelligence unit can further include a recommendation engine (discloses recommendation engine) for applying a machine learning technique to the environmental data from the data layer to generate predictions based on the environmental data. The cognitive intelligence unit can further include a risk model unit for applying one or more risk modelling techniques to the environmental data from the data layer. The risk model unit and the recommendation engine consume the enriched environmental data that is stored in the digital trust infrastructure unit, and the digital trust infrastructure unit employs a blockchain for storing the enriched environmental data).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the recommendation engine elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure for the same reasons as stated for claim 1.
Regarding Claim 14, Papadopoulos discloses …receive information identifying a computing system for energy management, the computing system having a set of hardware components associated with first energy information, a set of virtual machines associated with second energy information, and a set of software entities associated with third energy information (Papadopoulos, ¶ 4, One implementation of the present disclosure is a building automation system (BAS) for a building, according to some embodiments. In some embodiments, the BAS includes multiple devices including processing circuitry. The processing circuitry is configured to obtain time series data indicating reduced energy consumption of building equipment of the building, or green energy generation for the building, according to some embodiments. The processing circuitry is also configured to determine a corresponding carbon emissions reduction resulting from the reduced energy consumption of the building equipment or the green energy generation, according to some embodiments. The processing circuitry is also configured to create a carbon offset token responsive to the corresponding carbon emissions reduction being greater than a threshold, according to some embodiments. The processing circuitry is also configured to validate a new block of a first blockchain, according to some embodiments. In some embodiments, the carbon offset token is an attribute or data of the new block of the first blockchain, the first blockchain being limited from public access. In some embodiments, the processing circuitry is configured to provide the first blockchain as an input to a new block or a sidechain of a second blockchain. In some embodiments, the second blockchain is publicly accessible and includes the carbon offset token as a result of providing the first blockchain as an input to the new block or sidechain of the second blockchain), (Id., ¶ 111, HVAC device 1 is shown to communicate via network 508 and network 902 via network interface 912. Network interface 912 can be configured to communicate with network 508 and/or network 902. Network interface 912 can be one or more circuits configured to allow HVAC device 1 to communicate with network 508 and/or network 902. Network interface 912 can be configured to communicate via LANs, metropolitan area networks (MANs), and WANs. Network interface 912 can be configured to communicate via the Internet, Ethernet, Wi-Fi, a field bus, MODBUS, BACnet, CAN, near field communication (NFC), Zigbee, Bluetooth, and/or any other network and/or combination thereof. Network interface 912 can include one or more wired and/or wireless transceivers and/or receives (e.g., a Wi-Fi transceiver, a Bluetooth transceiver, a NFC transceiver, a cellular transceiver, etc.)), (Id., ¶ 53, Airside system 130 can deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and can provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) (discloses hardware components associated with first energy information) configured to measure attributes of the supply airflow. AHU 106 can receive input from sensors located within AHU 106 and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone), (Id., ¶ 239, Process 2500 includes obtaining equipment information associated with one or more physical or non-physical building equipment (step 2502), according to some embodiments. The communications interface 2302 can obtain equipment information. The equipment information can include ownership data and technical data. The equipment information can be associated with physical and/or non-physical building equipment. Physical building equipment can include, for example, hardware-based devices having a housing, circuitry, processors, memory, power supplies, and/or other physical hardware (h/w) components. Non-physical building equipment can include, for example, software-based devices such as virtual devices, digital twins, virtual machines, (discloses virtual machines) emulated equipment, device software (s/w) or other equipment that exists in a virtual or non-physical form. The communications interface 2302 can obtain the equipment information from the building agencies server 2330. The communications interface 2302 can provide the equipment information to the NFT generator 2318), (Id., ¶ 48, Using blockchain with HVAC devices can result in higher levels of security for a building management system (BMS) since a single point of failure is removed. If a cyber-attack is launched against the BMS, the cyber-attack would need to compromise the HVAC data chain of all the HVAC devices instead of just the data stored on a central server of the BMS. Using the HVAC data chain can result in a high byzantine fault tolerance. Further, there may be mass disintermediation due to consensus-driven decision-making associated with the HVAC data chain. Further, using the HVAC data chain may lower data corruption security breach uncertainties. Using blockchain can allow the HVAC devices to enter permanent and/or time based contracts or transactions among each other without any human intervention. In a building with HVAC devices, the HVAC devices can use blockchain to store and validate energy consumption information, load curtailment information, carbon credits, (discloses second and third energy information) transmit access rights and licensing information, transmit control actions that can have significant impact on the controlled environment, perform transaction negotiation between two HVAC devices, transport sensitive information across the HVAC device network, and can be used for any communication in building automation and control implementations that have environment requirements that require validation and security. The HVAC data chain can be used for any communication in a BMS (e.g., transport of sensitive information) and/or other control implementation that have validated environment requirements), (Id., ¶ 82, Referring now to FIG. 5, a system 500 of HVAC devices communicating via a network, with the HVAC devices storing an HVAC data chain 510, is shown, according to some embodiments. System 500 is shown to include a number of HVAC devices communicating via a network 508 (e.g., a distributed network). The number of HVAC devices are shown to include HVAC device 1, HVAC device 2, HVAC device 3, HVAC device 4, HVAC device 5, and HVAC device 6. There can be any number of HVAC devices in system 500. HVAC devices 1-6 can be any controller, actuator, or sensor that can communicate via network 508. In some embodiments, HVAC devices 1-6 are building gateways and/or various other building devices or building controllers. HVAC devices 1-6 can be the devices of smart connected things 204 and/or gateway 206 as described with reference to FIGS. 2-3 (e.g., controllers 226a-b, actuators 224, sensors 220, etc.) In some embodiments, HVAC devices 1-6 are VAV boxes of VAV units 116, AHU 106, chiller 102, and/or boiler 104 as described with reference to FIG. 1), (Id., ¶ 83, In some embodiments, HVAC devices 1-6 can be computing devices that utilize Software Agents. Software Agents are described with further reference to U.S. patent application Ser. No. 15/367,167 filed Dec. 1, 2016, the entirety of which is incorporated by reference herein (discloses software entities));
generate a digital twin of the computing system using the first energy information for simulation of the set of hardware components, the second energy information for simulation of the set of virtual machines, and the third energy information for simulation of the set of software entities (Id., ¶ 222, In some embodiments, the virtual environment server 2332 can generate a virtual environment (e.g., a digital twin) (discloses digital twin) for the building 10 and/or for the building model that pertains to building 10. For example, the virtual environment server 2332 can receive, from the communications interface 2302, the building model 2322. The virtual environment server 2332 can, using the building model 2322, generate the virtual environment for the building 10. The virtual environment server 2332 can include at least electronic device 2334. The electronic device 2334 can be or include at least one of a Head-Mounted Display (HMD), a pair of smart glasses, a mobile device, a virtual reality (VR) headset and/or any other computing device that a user can use to view or interact with the Metaverse), (Id., ¶ 82, Referring now to FIG. 5, a system 500 of HVAC devices communicating via a network, with the HVAC devices storing an HVAC data chain 510, is shown, according to some embodiments. System 500 is shown to include a number of HVAC devices communicating via a network 508 (e.g., a distributed network). The number of HVAC devices are shown to include HVAC device 1, HVAC device 2, HVAC device 3, HVAC device 4, HVAC device 5, and HVAC device 6. There can be any number of HVAC devices in system 500. HVAC devices 1-6 can be any controller, actuator, or sensor that can communicate via network 508. In some embodiments, HVAC devices 1-6 are building gateways and/or various other building devices or building controllers. HVAC devices 1-6 can be the devices of smart connected things 204 and/or gateway 206 as described with reference to FIGS. 2-3 (e.g., controllers 226a-b, actuators 224, sensors 220, etc.) In some embodiments, HVAC devices 1-6 are VAV boxes of VAV units 116, AHU 106, chiller 102, and/or boiler 104 as described with reference to FIG. 1), (Id., ¶ 53, Airside system 130 can deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and can provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) (discloses hardware components associated with first energy information) configured to measure attributes of the supply airflow. AHU 106 can receive input from sensors located within AHU 106 and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone), (Id., ¶ 53, Airside system 130 can deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and can provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) (discloses hardware components associated with first energy information) configured to measure attributes of the supply airflow. AHU 106 can receive input from sensors located within AHU 106 and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone), (Id., ¶ 239, Process 2500 includes obtaining equipment information associated with one or more physical or non-physical building equipment (step 2502), according to some embodiments. The communications interface 2302 can obtain equipment information. The equipment information can include ownership data and technical data. The equipment information can be associated with physical and/or non-physical building equipment. Physical building equipment can include, for example, hardware-based devices having a housing, circuitry, processors, memory, power supplies, and/or other physical hardware (h/w) components. Non-physical building equipment can include, for example, software-based devices such as virtual devices, digital twins, virtual machines, (discloses virtual machines) emulated equipment, device software (s/w) or other equipment that exists in a virtual or non-physical form. The communications interface 2302 can obtain the equipment information from the building agencies server 2330. The communications interface 2302 can provide the equipment information to the NFT generator 2318), (Id., ¶ 48, Using blockchain with HVAC devices can result in higher levels of security for a building management system (BMS) since a single point of failure is removed. If a cyber-attack is launched against the BMS, the cyber-attack would need to compromise the HVAC data chain of all the HVAC devices instead of just the data stored on a central server of the BMS. Using the HVAC data chain can result in a high byzantine fault tolerance. Further, there may be mass disintermediation due to consensus-driven decision-making associated with the HVAC data chain. Further, using the HVAC data chain may lower data corruption security breach uncertainties. Using blockchain can allow the HVAC devices to enter permanent and/or time based contracts or transactions among each other without any human intervention. In a building with HVAC devices, the HVAC devices can use blockchain to store and validate energy consumption information, load curtailment information, carbon credits, (discloses second and third energy information) transmit access rights and licensing information, transmit control actions that can have significant impact on the controlled environment, perform transaction negotiation between two HVAC devices, transport sensitive information across the HVAC device network, and can be used for any communication in building automation and control implementations that have environment requirements that require validation and security. The HVAC data chain can be used for any communication in a BMS (e.g., transport of sensitive information) and/or other control implementation that have validated environment requirements), (Id., ¶ 83, In some embodiments, HVAC devices 1-6 can be computing devices that utilize Software Agents. Software Agents are described with further reference to U.S. patent application Ser. No. 15/367,167 filed Dec. 1, 2016, the entirety of which is incorporated by reference herein (discloses software entities));
determine, using the digital twin of the computing system, a set of energy consumption metrics, for the computing system, associated with a set of candidate parameters (Id., ¶ 222, In some embodiments, the virtual environment server 2332 can generate a virtual environment (e.g., a digital twin) (discloses digital twin) for the building 10 and/or for the building model that pertains to building 10. For example, the virtual environment server 2332 can receive, from the communications interface 2302, the building model 2322. The virtual environment server 2332 can, using the building model 2322, generate the virtual environment for the building 10. The virtual environment server 2332 can include at least electronic device 2334. The electronic device 2334 can be or include at least one of a Head-Mounted Display (HMD), a pair of smart glasses, a mobile device, a virtual reality (VR) headset and/or any other computing device that a user can use to view or interact with the Metaverse), (Id., ¶ 86, HVAC devices 1-6 can monitor energy consumption, (discloses energy consumption metrics within HVAC device parameters) control lighting systems, control HVAC systems, or perform other functions within the BMS. HVAC devices 1-6 can be configured to optimize the systems they control based on energy efficiency, occupants comfort and productivity, and corporate or regulatory policies. HVAC devices 1-6 may be various HVAC devices, building lighting devices, building security devices (e.g., cameras, access control, etc.), safety devices (e.g., devices of a fire system), and/or devices of any other system of a building (e.g., building 10). In some embodiments, using temperature information from a room and/or zone as well as temperature data from an outside sensor, HVAC devices 1-6 can be configured to affect environmental changes in the room and/or zone. In some embodiments, HVAC devices 1-6 can cause dampers (e.g., actuators 526a-c) located in an air duct to be opened or closed, can activate heating, cooling, and/or air circulation equipment in the building and/or zone, and/or can perform any other control command that causes a room, zone, and/or building to be controlled to a particular temperature).
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose …A non-transitory computer-readable medium storing a set of instructions for wireless communication, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to:…; generate, using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters; and determine a set of updated energy consumption metrics associated with the set of candidate parameters based on the one or more recommendations; and select a particular recommendation, from the one or more recommendations, based on the set of updated energy consumption metrics; and transmit information identifying the particular recommendation.
However, Kumar discloses … A non-transitory computer-readable medium storing a set of instructions for wireless communication, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to … (Kumar, ¶ 71, FIG. 5 is a high-level block diagram of an electronic or computing device 400 that can be used with the embodiments disclosed herein of the data collection and processing system 10 of the present invention. Without limitation, the hardware, software, and techniques described herein can be implemented in digital electronic circuitry or in computer hardware that executes firmware, software, or combinations thereof. The implementation can include a computer program product (e.g., a non-transitory computer program tangibly embodied in a machine-readable storage device, for execution by, or to control the operation of, one or more data processing apparatuses, such as a programmable processor, one or more computers, one or more servers and the like));
generate, using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters; and determine a set of updated energy consumption metrics associated with the set of candidate parameters based on the one or more recommendations; and select a particular recommendation, from the one or more recommendations, based on the set of updated energy consumption metrics; and transmit information identifying the particular recommendation. (Kumar, ¶ 10, The system of the present invention can also include a computing layer for storing the data from the plurality of data sources prior to storing the data in the data layer. The digital trust infrastructure unit employs a blockchain for storing any combination of the environmental data, the enriched environmental data, the financial data, and one or more of the pre-defined techniques. Further, the cognitive intelligence unit can further include a recommendation engine (discloses recommendation engine) for applying a machine learning technique to the environmental data from the data layer to generate predictions based on the environmental data. The cognitive intelligence unit can further include a risk model unit for applying one or more risk modelling techniques to the environmental data from the data layer. The risk model unit and the recommendation engine consume the enriched environmental data that is stored in the digital trust infrastructure unit, and the digital trust infrastructure unit employs a blockchain for storing the enriched environmental data), (Id., ¶ 43, The data collection and processing system 10 of the present invention can be employed to create an environmental and financial infrastructure for allowing financial institutions to process environmental and/or financial data so as to provide financial, tax, accounting, consulting, and business related services (including risk modelling services) to clients. By way of example, and for purposes of simplicity, FIG. 3 shows an example of the data collection and processing system 10 configured for processing environmental data according to an exemplary technique. Those of ordinary skill in the art will readily recognize that the system 10 can be employed to gather, process and analyze many different types of data. The data can be provided by data sources 12 that provide environmental data associated with a structure, such as a building 54. In the illustrated embodiment, the data sources 12 can provide original or raw environmental data associated with various building associated parameters, such as for example, energy consumption, humidity, occupancy, air quality, water usage, and the like. The original environmental data can be communicated via the network 14 to the computing layer 18 and then to the data analysis module 16. Specifically, the original environmental data can be collected in the computing layer 18 and can be indicative of asset grade environmental data. An example of a software application that can be employed to ingest or collect the environmental data in the computing layer 18 is any of the suitable software application from Context Labs. The original environmental data stored in the computing layer 18 is then transferred or conveyed to the enrichment unit 22 of the data analysis module 16. The enrichment layer 22 can include for example the application interface unit 32 and the cognitive intelligence unit 34. In the application interface unit 32, suitable software, such as Nantum OS from Prescriptive Data, can store and analyze the environmental data received from the data sources 12 associated with the building 54. The enrichment layer 22 can recommend adjustments to be made to the building operational systems so as to improve overall building efficiency and tenant comfort. The application interface unit 32 can also include the financial subsystem 37, FIG. 2, for applying financial concepts and logic to the environmental data and for generating or extracting financial data therefrom. Further, environmental data from the third party sources 38 can also be provided to further enrich the original environmental data and optionally the financial data. The third party data 38 can include for example environmental data associated with the operations of the building 54 or similar or different types of buildings, specifications of equipment employed in the building 54, external environmental data such as geospatial, weather, temperature, humidity, wind, sun exposure and the like, spatial information regarding the building layout, power grid information, and maintenance information regarding one or more aspects of the physical plant. This list of third party data is merely exemplary and is not intended to be exhaustive. The third party data 38 is intended to improve the quality and integrity of the environmental data. The cognitive intelligence unit 34 can include techniques or models for synthesizing, improving or optimizing the original environmental data, including data associated with overall consumption and measuring of the overall consumption, and then subsequently analyzing the data using risk modelling, machine learning or artificial intelligence techniques. The cognitive intelligence unit 34 can derive insights relative to the environmental data for decision making, performance, optimization and risk management. The processed and analyzed (e.g., enriched) data in the enrichment unit 22 can then be conveyed back to the building 54 to adjust or control on or more building environmental or operational parameters and/or can be conveyed to the digital trust infrastructure unit 20. When conveyed back to the building 54, the various systems in the building can be modified so as to operate the physical facility in a more cost and environmentally friendly and efficient manner).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the recommendation engine elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure for the same reasons as stated for claim 1.
Regarding Claim 15, the combination of Papadopoulos and Kumar discloses …The non-transitory computer-readable medium of claim 14…
Papadopoulos further discloses …wherein the one or more instructions further cause the device to: monitor an actual energy consumption of the computing system (Id., ¶ 178, The BAS controller 1902 can also obtain utility data 1914 (e.g., from a utility provider, from an energy provider, etc.). In some embodiments, the utility data 1914 includes historical metering data, historical electrical energy consumption of a particular customer or building, historical natural gas consumption of a particular customer or building, a monetary cost per unit of electrical energy or natural gas, a carbon emissions cost per unit of electrical energy or natural gas, etc. In some embodiments, the utility data includes an amount of electrical energy that is consumed by a building (e.g., the building 10), multiple buildings 10 of a campus, a total energy consumption of a campus of buildings 10, energy consumption of a particular piece or unit of building equipment (e.g., chillers, VRF units, boilers, etc.), or electrical energy consumption of any other building, equipment, (discloses energy consumption of computing system) collection of spaces or equipment, collections of buildings, etc. In some embodiments, the BAS controller 1902 is also configured to obtain data from one or more electrical meters that are configured to measure metered amounts of electrical energy, natural gas consumption, etc., of any building, collection of buildings, equipment, collection of equipment, etc. In some embodiments, the BAS controller 1902 is configured to generate a baseline for the building 10 (e.g., baseline or expected energy consumption) using the utility data 1914 so that subsequent changes in electrical energy consumption (e.g., reduction in purchased electricity from a power grid, improved efficiency control algorithms that result in reduced electrical energy consumption, reduced amount of electrical energy consumption due to installation of higher efficiency equipment, reduced amount of electrical energy consumption due to incentive programs for occupants to use less energy, reduced amount of electrical energy consumption due to installation of higher efficiency energy consuming devices such as lighting devices, etc.) can be compared to the baseline to identify a corresponding reduction in carbon emissions);
compare the actual energy consumption of the computing system with a simulated energy consumption of the digital twin of the computing system (Id., ¶ 205, Process 2200 includes providing the hybrid blockchain to a public blockchain as a side chain input (e.g., as a single token) and allowing wallets or users of the public blockchain to sell, burn, purchase, or exchange (e.g., for an NFT, a digital asset, a metaverse asset, a metaverse digital twin of building equipment, an NFT compliance award, etc.) the carbon offset tokens in a public marketplace (step 2212), according to some embodiments. In some embodiments, performing step 2212 allows users with wallet addresses on the public blockchain to own carbon offset tokens that can be used as a coin or form of cryptocurrency), (Id., ¶ 222, the virtual environment server 2332 can generate a virtual environment (e.g., a digital twin) for the building 10 and/or for the building model that pertains to building 10. For example, the virtual environment server 2332 can receive, from the communications interface 2302, the building model 2322. The virtual environment server 2332 can, using the building model 2322, generate the virtual environment for the building 10. The virtual environment server 2332 can include at least electronic device 2334. The electronic device 2334 can be or include at least one of a Head-Mounted Display (HMD), a pair of smart glasses, a mobile device, a virtual reality (VR) headset and/or any other computing device that a user can use to view or interact with the Metaverse), (Id., ¶ 191, Referring to FIGS. 19 and 20, the MV application 1922 may provide measurement and verification data to the carbon offset token determination manager 1952 which may use the measurement and verification data to determine the carbon offset tokens. In some embodiments, the CUP application 1920 is configured to provide energy savings data that is determined based on facility improvement measures (FIMs) such as upgrades of equipment or HVAC sub-systems or system, repairing faults of building HVAC equipment, etc. In some embodiments, the carbon offset token determination manager 1952 is configured to use the energy savings data (e.g., relative to a baseline energy consumption of the building 10) in order to determine the carbon offset tokens (e.g., to determine the carbon emissions reductions due to the energy savings). As described herein, the carbon offset determinations as performed by the offset determination manager 1938 or the offset value estimator 1956 can be performed by comparing energy usage of the building 10, (discloses comparing energy consumption) or components of the building 10 (e.g., building equipment) to offset thresholds (e.g., a required threshold or amount of carbon offset required in order to obtain a carbon offset token) in order to determine the carbon offsets. In some embodiments, the carbon offset determinations are performed based on the data from the metering devices 1912, the energy generation of various green energy sources, equipment data, demand reduction, the measurement and verification data provided by the MV application 1922, demand response of the building 10 or a campus of buildings 10 relative to a baseline);
and update one or more characteristics of the digital twin of the computing system based on comparing the actual energy consumption with the simulated energy consumption (Id., ¶ 191, Referring to FIGS. 19 and 20, the MV application 1922 may provide measurement and verification data to the carbon offset token determination manager 1952 which may use the measurement and verification data to determine the carbon offset tokens. In some embodiments, the CUP application 1920 is configured to provide energy savings data that is determined based on facility improvement measures (FIMs) such as upgrades of equipment or HVAC sub-systems or system, repairing faults of building HVAC equipment, etc. In some embodiments, the carbon offset token determination manager 1952 is configured to use the energy savings data (e.g., relative to a baseline energy consumption of the building 10) in order to determine the carbon offset tokens (e.g., to determine the carbon emissions reductions due to the energy savings). As described herein, the carbon offset determinations as performed by the offset determination manager 1938 or the offset value estimator 1956 can be performed by comparing energy usage of the building 10, or components of the building 10 (e.g., building equipment) to offset thresholds (e.g., a required threshold or amount of carbon offset required in order to obtain a carbon offset token) in order to determine the carbon offsets. In some embodiments, the carbon offset determinations are performed based on the data from the metering devices 1912, the energy generation of various green energy sources, equipment data, demand reduction, the measurement and verification data provided by the MV application 1922, demand response of the building 10 or a campus of buildings 10 relative to a baseline), (Id., ¶ 224, The communications interface 2302 can obtain, from the building agencies server 2330, equipment information associated with one or more pieces of building equipment that can be included, implemented, associated with or pertain to a building (e.g., building 10). The equipment information can pertain to the physical piece of building equipment and/or the virtual information that pertains to the piece of building equipment. The building agencies server 2330 can be or include a digital platform, a cloud computing system or database. In some embodiments, the equipment information stored by the building agencies server 2330 can be stored in, retrieved from, or processed in the context of digital twins. In some such embodiments, the digital twins may be provided within an infrastructure such as those described in U.S. patent application Ser. No. 17/134,661 filed Dec. 28, 2020, 63/289,499 filed Dec. 14, 2021, and Ser. No. 17/537,046 filed Nov. 29, 2021, the entireties of each of which are incorporated herein by reference), (Id., ¶ 96, Carbon credits server 608 is shown to include carbon credit updater 612 which can be configured to communicate with HVAC devices 1 and 3-6 (carbon credits server 608 may take the place of HVAC device 2 in this embodiment). Carbon credit updater 612 can be configured to receive carbon credit requests and distribute carbon credits to HVAC devices 1 and 3-6 upon validating HVAC devices 1 and 3-6 have earned the requested carbon credits as a result of energy reduction or other sustainability-related activities performed by HVAC devices 1 and 3-6. Further, carbon credit updater 612 can facilitate interactions with user device 638. In some embodiments, carbon credit updater 612 can generate a block for HVAC data chain 510 that includes carbon credit information).
Regarding Claim 17, the combination of Papadopoulos and Kumar discloses …The non-transitory computer-readable medium of claim 14…
Papadopoulos further discloses …wherein the one or more instructions further cause the device to: identify a carbon footprint associated with the computing system based on the set of energy consumption metrics (Id., ¶ 9, In some embodiments, the corresponding carbon emissions reduction is either (i) measured based on data from a metering device and utility data, or (discloses carbon footprint) (ii) estimated based on equipment data of the building equipment, space data of a space of the building, and a model of the building equipment. In some embodiments, the green energy generation for the building includes any of BioEnergy, GeoThermal energy, solar photovoltaic energy, hydropower energy, ocean energy, or wind energy);
and identify a set of carbon offsets for mitigating the carbon footprint (Id., ¶ 12, Another implementation of the present disclosure is a method for incentivizing carbon emissions reduction of a building, according to some embodiments. In some embodiments, the method includes generating a carbon offset token based on equipment data. In some embodiments, the carbon offset token represents a quantified amount of carbon emissions reduction resulting from a reduced amount of energy consumption (discloses carbon offsets) or an amount of green energy generation for the building indicated by the equipment data. In some embodiments, the method includes validating a new block of a first blockchain. In some embodiments, the carbon offset token is an attribute or data of the new block of the first blockchain. In some embodiments, the first blockchain is limited from public access. In some embodiments, the method includes providing the first blockchain as an input to a new block or a sidechain of a second blockchain. In some embodiments, the second blockchain is publicly accessible and includes the carbon offset token as a result of providing the first blockchain as an input to the new block or sidechain of the second blockchain), (Id., ¶ 15, In some embodiments, the carbon offset token includes multiple attributes. In some embodiments, the attributes include an indication of an offset type of the carbon emissions reduction, an indication of a threshold of the carbon emissions reduction used to generate the carbon offset token, a date and time that the carbon offset token is created, whether the carbon offset token is created based on meter data or estimated data, one or more addresses of validators of the carbon offset token, and the time series data used to create the carbon offset token. In some embodiments, the carbon offset token is exchangeable on the second blockchain for a tax refund for an owner of the carbon offset token).
Regarding Claim 18, the combination of Papadopoulos and Kumar discloses …The non-transitory computer-readable medium of claim 17…
Papadopoulos further discloses …wherein the one or more instructions further cause the device to: automatically process a transaction for the set of carbon offsets (Id., ¶ 88, HVAC devices 1-6 are shown to each include HVAC data chain 510. HVAC data chain 510 can be a chain of one or more data blocks where each data block is linked to a previous block, thus, forming a chain. The chain may be a chain of sequentially ordered blocks. In some embodiments, HVAC data chain 510 is a blockchain database. HVAC devices 1-6 can exchange building data that they collect via controllers 522a-d, sensors 524a-c, and/or actuators 526a-c via HVAC data chain 510. In some embodiments, HVAC data chain 510 includes information regarding energy consumption, load curtailment, greenhouse gas emissions, carbon credit entitlement or carbon credit balances, or other information that can be used to validate or transact carbon credits (discloses processing a transaction for carbon offsets) for HVAC devices 1-6, access to data for HVAC devices 1-6, and various other building related information. HVAC data chain 510 can be the same on each of HVAC devices 1-6. This allows each device of system 500 to have access to the same information. Further, this allows each of HVAC devices 1-6 to communicate with each other directly rather than relying on a central computing system), (Id., ¶ 101, Referring now to FIG. 7, another embodiment of system 500 is shown for transferring carbon credits to one HVAC device from another HVAC device, according to an exemplary embodiment. This may occur, for example, when carbon credits server 608 allocates an amount of carbon credits earned by a HVAC subsystem or group of HVAC devices (e.g., carbon credits A) to one HVAC device in the group (e.g., HVAC device 6) and that HVAC device distributes portions of the carbon credits earned by each individual HVAC device (e.g., carbon credits A1, carbon credits A2, etc.) to the other HVAC devices in the group (e.g., HVAC device 1). In FIG. 7, HVAC device 1 can be configured to send carbon credits A1 request 706 to HVAC device 6. HVAC device 6 is shown to be configured to reply to carbon credits A1 request 706 by sending carbon credits A1 708 to HVAC device 1. In some embodiments, transferring carbon credits A1 708 from HVAC device 1 to HVAC device 6 includes generating a carbon credits transaction and adding the carbon credits transaction to a block of HVAC data chain 510).
Regarding Claim 19, the combination of Papadopoulos and Kumar discloses …The non-transitory computer-readable medium of claim 14…
Papadopoulos further discloses …wherein the one or more instructions further cause the device to: detect, based on the set of energy consumption metrics, a threshold change to an energy consumption of the computing system (Id., ¶ 191, Referring to FIGS. 19 and 20, the MV application 1922 may provide measurement and verification data to the carbon offset token determination manager 1952 which may use the measurement and verification data to determine the carbon offset tokens. In some embodiments, the CUP application 1920 is configured to provide energy savings data that is determined based on facility improvement measures (FIMs) such as upgrades of equipment or HVAC sub-systems or system, repairing faults of building HVAC equipment, etc. In some embodiments, the carbon offset token determination manager 1952 is configured to use the energy savings data (e.g., relative to a baseline energy consumption of the building 10) in order to determine the carbon offset tokens (e.g., to determine the carbon emissions reductions due to the energy savings). As described herein, the carbon offset determinations as performed by the offset determination manager 1938 or the offset value estimator 1956 can be performed by comparing energy usage of the building 10, or components of the building 10 (e.g., building equipment) to offset thresholds (e.g., a required threshold or amount of carbon offset required in order to obtain a carbon offset token) (discloses threshold change to energy consumption) in order to determine the carbon offsets. In some embodiments, the carbon offset determinations are performed based on the data from the metering devices 1912, the energy generation of various green energy sources, equipment data, demand reduction, the measurement and verification data provided by the MV application 1922, demand response of the building 10 or a campus of buildings 10 relative to a baseline.
and wherein the one or more instructions, that cause the device to generate the one or more recommendations, cause the device to: predict a component, of the set of hardware components, the set of virtual machines, or the set of software entities, responsible for the threshold change to the energy consumption (Id., ¶ 91, In some embodiments, the blocks of HVAC data chain 510 make up a record of data for a building and/or for various devices. For example, the blocks of HVAC data chain 510 may each comprise HVAC data associated with the building. Each block may store HVAC data of a particular device of HVAC devices 1-6. Together, in HVAC data chain 510, the blocks may make up a record of all the HVAC data associated with the building. In another example, the blocks each include information pertaining to energy consumption or carbon credit information for HVAC devices 1-6. On their own, the blocks may only indicate the energy consumption or carbon credit information for one of HVAC devices 1-6. However, together, in HVAC data chain 510, the blocks may represent a record of all energy consumption or carbon credits for a particular building and/or for HVAC devices 1-6. In yet another example, the blocks of HVAC data chain 510 may each individually include information pertaining to a particular zone of a building. HVAC devices 1-6 can be configured to generate and/or retrieve the data and adding them to a block of HVAC data chain 1-6. Individually, the blocks of HVAC data chain 510 may represent HVAC data for only particular zones, however, together, in HVAC data chain 510, the blocks may represent HVAC data for a plurality of zones and/or for a particular building that includes the plurality of zones. This concept of storing a complete record of information in pieces in blocks of HVAC data chain 510 can be used to store any kind of data described herein.
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose … and generate a recommendation for mitigating the threshold change to the energy consumption based on predicting the component responsible for the threshold change to the energy consumption.
However, Kumar discloses …and generate a recommendation for mitigating the threshold change to the energy consumption based on predicting the component responsible for the threshold change to the energy consumption (Kumar, ¶ 57, The data collection and processing system 10 of the present invention integrates different technologies in a cloud based manner to collect environmental data from various data sources 12 and thus form the backbone of an environmental accounting infrastructure. The system 10 can process, enrich, audit and authenticate the environmental data in a highly automated manner. The cognitive intelligence unit 34 of the data analysis module 16 can derive granular insights and help enterprises make predictions for how best to optimize the building systems and operations, so as to help mitigate the overall environmental impact of the building. The reporting functions of the post-processing unit 24 and the financial verification unit 70 can allow the attestation body (e.g., accounting firm) to attest and certify environmental related disclosures for financial and non-financial reporting and compliance within the context of standard accounting frameworks), (Id., ¶ 10, The system of the present invention can also include a computing layer for storing the data from the plurality of data sources prior to storing the data in the data layer. The digital trust infrastructure unit employs a blockchain for storing any combination of the environmental data, the enriched environmental data, the financial data, and one or more of the pre-defined techniques. Further, the cognitive intelligence unit can further include a recommendation engine for applying a machine learning technique to the environmental data from the data layer to generate predictions based on the environmental data. The cognitive intelligence unit can further include a risk model unit for applying one or more risk modelling techniques to the environmental data from the data layer. The risk model unit and the recommendation engine consume the enriched environmental data that is stored in the digital trust infrastructure unit, and the digital trust infrastructure unit employs a blockchain for storing the enriched environmental data), (Id., ¶ 48, FIG. 4 is a schematic block diagram showing the post-processing unit 24 configured to optionally include or employ one or more selected units for generating one or more reports directed to a selected system capability or functionality of the enterprise. For example, regarding the emission accounting capability, the post-processing unit 24 can be configured to include an optional emissions accounting unit 80 that can include one or more software applications and associated hardware for processing the enriched environmental data to aggregate data related to the energy consumed or used by the enterprise, such as a structure or collection of structures (e.g., buildings), equipment, facility, business, company, operation, organization, country or entity, to compute various emissions-related metrics using standard emissions factors available from third-party sources, for example, the emissions factors provided by the International Energy Agency (IEA); to track emissions relative to established emissions goals for a specific building, equipment, enterprise, or country; and to determine the overall emissions related liabilities of the enterprise. Specifically, the emissions accounting unit 80 can determine or calculate the emissions of an enterprise or building, determine and track energy consumption of the enterprise, and/or determine or track the overall emissions goals for the enterprise based on the environmental data and/or third party data following various known climate-related accounting standards and frameworks. The climate related standards can include for example standards and frameworks established by greenhouse gas protocols, sustainability accounting standards boards (SASB), task forces on climate-related financial disclosures (TCFD), global logistics emissions council (GLEC), and global reporting institute (GRI). The emissions accounting unit 80 can also be configured to track emissions generated from their operations with the required level of granularity or specificity for deciding specific to reducing emissions through various known carbon management strategies. For example, the emissions accounting unit 80 can use the environmental data to determine the biggest emission generators in the building and then take selected emissions remediation actions (e.g., climate actions), such as for example upgrade the emission sources, initiate repair or maintenance of selected building components, and to forecast future investment needs. The climate actions can also include for example initiatives related to carbon reduction (e.g., energy efficiency), carbon removal, carbon offsetting, carbon capture and storage, and the like).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the recommendation engine elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure for the same reasons as stated for claim 1.
Regarding Claim 20, the combination of Papadopoulos and Kumar discloses …The non-transitory computer-readable medium of claim 14…
While suggested in at least Fig. 1 and related text, Papadopoulos does not explicitly disclose … wherein the one or more instructions, that cause the device to generate the one or more recommendations, cause the device to: generate a recommendation for optimizing energy consumption of the computing system across a set of computing tasks.
However, Kumar discloses … wherein the one or more instructions, that cause the device to generate the one or more recommendations, cause the device to: generate a recommendation for optimizing energy consumption of the computing system across a set of computing tasks (Kumar, ¶ 46, The data stored in the blocks of the blockchain 20A can be retrieved and processed by the post-processing unit 24 to generate one or more reports, such as environmental or financial reports, or other types of reports, via one or more suitable report generation applications. The environmental data can be employed to identify and analyze the building units that are generating emissions and how best to reduce the emissions and how the building is being powered. From this environmental data, the data analysis module 16 can determine the operational health of the building and associated systems, and perform a risk analysis on the physical building and associated external and internal climate. The reports can include among other things reports on green house gas emissions, green house gas optimization, carbon emission setting and optimization, risks and associated controls, transaction settlements, net-zero emissions compliance, and carbon pricing. The post-processing unit 24 can also optionally create if desired a reporting dashboard. The reports and the dashboard can be displayed in the display region 50, FIG. 2. The enrichment unit 22 can also include an optional financial analysis unit 74, similar to the financial subsystem 37, that can reside for example as part of the cognitive intelligence unit 34 or as a separate component or unit. The financial analysis unit 74 can analyze the data received from the digital trust infrastructure unit 20 to identify, process and analyze certain financial aspects of the environmental data. For example, the data can be processed using standard financial or accounting rules, logic and models and techniques. Also the financial analysis unit 74 can determine the carbon footprint associated with the environmental data. The financial analysis unit 74 can be used in place of the financial subsystem 37 or can be used in conjunction with the financial subsystem 37), (Id., ¶ 10, The system of the present invention can also include a computing layer for storing the data from the plurality of data sources prior to storing the data in the data layer. The digital trust infrastructure unit employs a blockchain for storing any combination of the environmental data, the enriched environmental data, the financial data, and one or more of the pre-defined techniques. Further, the cognitive intelligence unit can further include a recommendation engine for applying a machine learning technique to the environmental data from the data layer to generate predictions based on the environmental data. The cognitive intelligence unit can further include a risk model unit for applying one or more risk modelling techniques to the environmental data from the data layer. The risk model unit and the recommendation engine consume the enriched environmental data that is stored in the digital trust infrastructure unit, and the digital trust infrastructure unit employs a blockchain for storing the enriched environmental data), (Id., ¶ 48, FIG. 4 is a schematic block diagram showing the post-processing unit 24 configured to optionally include or employ one or more selected units for generating one or more reports directed to a selected system capability or functionality of the enterprise. For example, regarding the emission accounting capability, the post-processing unit 24 can be configured to include an optional emissions accounting unit 80 that can include one or more software applications and associated hardware for processing the enriched environmental data to aggregate data related to the energy consumed or used by the enterprise, such as a structure or collection of structures (e.g., buildings), equipment, facility, business, company, operation, organization, country or entity, to compute various emissions-related metrics using standard emissions factors available from third-party sources, for example, the emissions factors provided by the International Energy Agency (IEA); to track emissions relative to established emissions goals for a specific building, equipment, enterprise, or country; and to determine the overall emissions related liabilities of the enterprise. Specifically, the emissions accounting unit 80 can determine or calculate the emissions of an enterprise or building, determine and track energy consumption of the enterprise, and/or determine or track the overall emissions goals for the enterprise based on the environmental data and/or third party data following various known climate-related accounting standards and frameworks. The climate related standards can include for example standards and frameworks established by greenhouse gas protocols, sustainability accounting standards boards (SASB), task forces on climate-related financial disclosures (TCFD), global logistics emissions council (GLEC), and global reporting institute (GRI). The emissions accounting unit 80 can also be configured to track emissions generated from their operations with the required level of granularity or specificity for deciding specific to reducing emissions through various known carbon management strategies. For example, the emissions accounting unit 80 can use the environmental data to determine the biggest emission generators in the building and then take selected emissions remediation actions (e.g., climate actions), such as for example upgrade the emission sources, initiate repair or maintenance of selected building components, and to forecast future investment needs. The climate actions can also include for example initiatives related to carbon reduction (e.g., energy efficiency), carbon removal, carbon offsetting, carbon capture and storage, and the like).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos to include the recommendation engine elements of Kumar in the analogous art of collecting and storing environmental data in a digital trust model, and processing the data using an accounting infrastructure for the same reasons as stated for claim 1.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Papadopoulos in view of Kumar and in further view of Ramanasankaran et al., U.S. Publication No. 2023/0358429 [hereinafter Ramanasankaran].
Regarding Claim 16, the combination of Papadopoulos and Kumar discloses …The non-transitory computer-readable medium of claim 14…
Papadopoulos further discloses …wherein the one or more instructions further cause the device to: monitor an actual energy consumption of the computing system (Id., ¶ 70, Data analyzer 214 can be configured to perform data processing on data received from smart connected things 204, various components of cloud platform 202, and the Internet. Data analyzer 214 can be configured to perform parallel data analytics on received data and in some embodiments and/or use a software framework such Hadoop to perform the data processing. Data analyzer 214 can be configured to generate business intelligence metrics. Business intelligence metrics can be generated based on the received data. In some embodiments, the business intelligence metrics indicate power consumption of a building, predicted operating costs of new equipment, equipment replacements, and/or any other intelligence metric that can be generated from the received data. Further, data analyzer 214 can be configured to perform data mining to determine various patterns in large data sets that data analyzer 214 can receive and/or collect. Further, data analyzer 214 can be configured to perform any kind of time series data analysis (e.g., data cleansing, data extrapolation, interpolation, averaging). Time series data analysis can be temperatures of a zone collected by a sensors of building 10 e.g., sensors 22. For example, data analyzer 214 can “cleanse” the time series data by compressing the data points based on an upper threshold and/or a lower threshold);
While suggested in at least Fig. 1 and related text of Papadopoulos, the combination of Papadopoulos and Kumar does not explicitly disclose … and identify a system anomaly associated with the computing system based on monitoring the actual energy consumption of the computing system and based on simulated energy consumption of the digital twin of the computing system; and wherein the one or more instructions, that cause the device to generate the one or more recommendations, cause the device to: generate the one or more recommendations based on identifying the system anomaly.
However, Ramanasankaran discloses … and identify a system anomaly associated with the computing system based on monitoring the actual energy consumption of the computing system and based on simulated energy consumption of the digital twin of the computing system (Ramanasankaran, ¶ 274, In step 2308, the building data platform 100 can identify a combination of triggers and actions that maximizes a reward. The building data platform 100 can search the simulated combinations of triggers and/or actions to identify a trigger and/or action that maximizes a reward and/or minimizes a reward. In some embodiments, the building data platform 100 uses a policy gradient and value function instead of brute force to try out combinations of the triggers and/or actions in the steps 2306-2308), (Id., ¶ 275, In some embodiments, the building data platform 100 can identify the operations for the triggers and/or actions. For example, the operation could be comparing a measurement to a threshold, determining whether a measurement is less than a threshold, determining whether a measurement is greater than the threshold, determining whether the measurement is not equal to the threshold, etc.), (Id., ¶ 276, In step 2310, the building data platform 100 can generate a digital twin for the entity. The entity can include (or reference) the graph 529 and include an agent that operates the triggers and/or actions. The triggers and/or actions can operate based on the graph 529 and/or based on data received building equipment, e.g., the building subsystems 122);
and wherein the one or more instructions, that cause the device to generate the one or more recommendations, cause the device to: generate the one or more recommendations based on identifying the system anomaly (Id., ¶ 236, One digital twin may have trigger conditions such as, “when the outside temperature is x.sub.0, “when the inside humidity is x%,” “when an AI-driven algorithm’s threshold is reached,” and “when it is a certain day of the week.” In responsive to one or multiple triggers being met, the digital twin can perform actions (e.g., capabilities of a device either inherent and/or digital twin enhanced). The actions can include setting a setpoint to a value x.sub.0. The actions can be to run a fan for x minutes. The actions can be to start an AI-driven energy saving schedule. The actions can be to change a mode status to an away status. In some embodiments, the building data platform 100 can user other digital twins to simulate a reward for various values of the triggers and/or actions. The reward can be optimized to determine values for the parameters of the triggers and/or actions), (Id., ¶ 244, The building data platform 100 can generate accumulated training data. The accumulated training data can include the values of the parameters ε.sub.1 and ε.sub.2, the state of the AHU digital twin 1204 for each value of the parameters, and the energy score and comfort violation score for each state. In some embodiments, the triggers and/or actions that can be recommended for the thermostat digital twin can be determined by observing the responses of other digital twins on perturbed thresholds of existing triggers and/or actions).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy metrics elements of Papadopoulos and the recommendation engine elements of Kumar to include the anomaly detection elements of Ramanasankaran in the analogous art of a building data platform with digital twin-based diagnostic routines.
The motivation for doing so would have been “reducing carbon emissions for a building, improving a sustainability score, [and] driving the building towards net zero emissions production, etc.” [Ramanasankaran, ¶ 326], wherein such improvements would have benefitted Kumar’s method which seeks to “improve overall building efficiency and tenant comfort” [Kumar, ¶ 43], and wherein such improvements would further benefit Papadopoulos’ method which seeks to improve “efficiency control algorithms that result in reduced electrical energy consumption, reduced amount of electrical energy consumption due to installation of higher efficiency equipment, reduced amount of electrical energy consumption due to incentive programs for occupants to use less energy, [and] reduced amount of electrical energy consumption due to installation of higher efficiency energy consuming devices such as lighting devices, etc…” [Ramanasankaran, ¶ 326; Kumar, ¶ 43; Papadopoulos, ¶ 178].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Noziere, U.S. Publication No. 2021/0055750, discloses a method for optimizing the energy expenditure and comfort of a building.
Billeter et al., U.S. Publication No. 2023/0090136, discloses a performance measuring system measuring sustainable development relevant properties of an object, and method thereof.
Sepulveda et al., U.S. Publication No. 2023/0304664, discloses gas turbine predictive emissions modeling, reporting, and model management via a remote framework.
Any inquiry concerning this communication or earlier communications from the examiner
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should be directed to NICHOLAS D BOLEN whose telephone number is (408)918-7631. The examiner can normally be reached Monday - Friday 8:00 AM - 5:00 PM PST.
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/NICHOLAS D BOLEN/ Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624