DETAILED ACTION
Claims 1-20 are pending.
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 .
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jain, Achin, et al. "Digital twins for efficient modeling and control of buildings an integrated solution with scada systems." 2018 Building Performance Analysis Conference and SimBuild. 2018 (hereinafter Jain).
Regarding claims 1-20, Jain discloses all the claimed limitations, as outlined below.
Claim 1. A building management system for a building comprising one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to: ingest event information from at least one of a building system or an external computing system (Pages 799-801 - - Events from more than one building are utilized as input to the digital twin and other real controllers);
enrich the event information based on a digital twin associated with the event information, wherein enriching the event information includes adding contextual information to the event information based on the digital twin to generate enriched event information (Pages 799-801 - - The event data from more than one building are combined with the digital twin data);
generate a predicted parameter that will result from a control decision for operating at least one of the building system or a different building system based on the enriched event information; and modify the control decision based on the predicted parameter (predictions/forecasting regarding the control of the campus/multiple buildings are generating). See figure 1 below.
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2. The system of claim 1, wherein the instructions further cause the one or more processors to: receive building information model (BIM) data corresponding to the building; and at least one of generate or enrich the digital twin based on the BIM data (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
3. The system of claim 2, wherein the BIM data comprises multiple BIM files received from multiple source (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
4. The system of claim 2, wherein the instructions further cause the one or more processors to ingest the event information from the BIM data (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
5. The system of claim 1, wherein the event information is ingested from the external computing system and comprises information relating to at least one of a transit action, energy usage, marginal emissions rates, electric prices, weather information, user schedules, or user behavior (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
6. The system of claim 1, wherein the predicted parameter is one of an energy parameter, an emissions parameter, an occupancy parameter, or a parameter associated with occupant comfort (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
7. The system of claim 1, wherein the predicted parameter is generated using a machine learning model (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
8. The system of claim 1, wherein the instructions cause the one or more processors to: estimate an occupancy schedule for a space of the building; predict an energy usage associated with the space using the occupancy schedule (Pages 799-801 - - BIM data is correlated with twin data of one or more building);
and generate the control decision using the predicted energy usage, wherein at least one of estimating the occupancy schedule or predicting the energy usage is performed using the enriched event information (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
9. The system of claim 8, wherein the instructions are further configured to cause the one or more processors to generate the control decision based on both the predicted energy usage and a comfort goal or parameter relating to a comfort of occupants of the space (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
10. The system of claim 1, wherein the instructions further cause the one or more processors to generate or modify, using the enriched event information, at least one of: a trigger associated with the digital twin or a different digital twin defining a rule that causes an action to be executed; or the action to be executed upon satisfaction of the trigger (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
11. The system of claim 1, wherein the digital twin is a virtual representation of a space of the building, an event associated with or occurring in the building, equipment of the building, or people associated with the building (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
12. A building management system comprising one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to: receive input data for a building from a data source, the input data generated at least in part prior to or during construction of the building or another building sharing one or more characteristics similar to the building (Pages 799-801 - - digital twins are created based on as built or existent data);
generate or modify a digital twin of the building based on the input data (Pages 799-801 - - Events from more than one building are utilized as input to the digital twin and other real controllers);
ingest additional data from at least one of a building system or an external computing system; and enrich the digital twin by updating the digital twin based on the additional data (Pages 799-801 - - The event data from more than one building are combined with the digital twin data). See figure 1 below.
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13. The system of claim 12, wherein the input data is building information model (BIM) data for a BIM model (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
14. The system of claim 12, wherein the instructions further cause the one or more processors to: ingest event information from at least one of the building system or an external computing system (Pages 799-801 - - BIM data is correlated with twin data of one or more building);
enrich the event information based on the digital twin, wherein enriching the event information includes adding contextual information to the event information based on the digital twin to generate enriched event information (Pages 799-801 - - BIM data is correlated with twin data of one or more building);
generate a predicted parameter that will result from a control decision for operating the building system based on the enriched event information; and modify the control decision based on the predicted parameter (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
15. The system of claim 12, wherein the digital twin comprises a first digital twin, and wherein the instructions cause the one or more processors to receive the input data from a second digital twin generated using data generated at least in part prior to or during construction of the building or another building sharing one or more characteristics similar to the building (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
16. The system of claim 15, wherein the second digital twin is generated by a first party, and the first digital twin is generated by a second party different than the first party (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
17. The system of claim 12 wherein the instructions further cause the one or more processors to: process the input data using a sustainability model to predict one or more parameters relating to a predicted energy usage or carbon production of the building (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
18. The system of claim 17, wherein the instructions further cause the one or more processors to: update the digital twin using the one or more predicted parameters (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
19. The system of claim 17, wherein the instructions further cause the one or more processors to: generate a recommendation for reducing at least one of energy usage or carbon production using the one or more predicted parameters (Pages 799-801 - - BIM data is correlated with twin data of one or more building).
20. A method comprising: receiving input data for a building from a data source, the input data generated at least in part prior to or during construction of the building or another building sharing one or more characteristics similar to the building (Pages 799-801 - - digital twins are created based on as built or existent data);
generating or modify a digital twin of the building based on the input data (Pages 799-801 - - Events from more than one building are utilized as input to the digital twin and other real controllers);
ingesting additional data from at least one of a building system or an external computing system; and enriching the digital twin by updating the digital twin based on the additional data (Pages 799-801 - - The event data from more than one building are combined with the digital twin data).
See Figure 1 below.
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Citation of Pertinent Prior Art
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
D’Oca, Simona, and Tianzhen Hong. "Occupancy schedules learning process through a data mining framework." Energy and Buildings 88 (2015): 395-408.
Qiuchen Lu, Vivi, et al. "Developing a dynamic digital twin at a building level: Using Cambridge campus as case study." (2019).
Lu, Qiuchen, et al. "From BIM towards digital twin: Strategy and future development for smart asset management." International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing. Cham: Springer International Publishing, 2019.
Khajavi, Siavash H., et al. "Digital twin: vision, benefits, boundaries, and creation for buildings." IEEE access 7 (2019): 147406-147419.
Chao et al., US Patent Application Publication No. 2013/0338972 – relates to BIM data model.
Conclusion
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/CARLOS R ORTIZ RODRIGUEZ/ Primary Examiner, Art Unit 2119