DETAILED ACTION
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 (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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6-14, and 16-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Harvey et al. US 2024/0338602 (“Harvey”).
Harvey discloses:
A method comprising:
receiving, by at least one processor, data descriptive of operation of equipment (e.g., [0049], [0055]);
storing, by the at least one processor, the data in a data store (e.g., Fig. 2, [0049], [0055]);
building, by the at least one processor, a digital twin of the equipment using the data (e.g., Fig. 1 #110, [0049], [0055]), the building comprising:
training at least one machine learning (ML) model on at least a portion of the data in the data store (e.g., Fig. 1 #125, “Training Process”); and
building a model accessible by a user interface (UI) and comprising at least one parameter determined by the trained ML model (e.g., Fig. 1 #125, [0058]-[0059]), wherein the model includes at least one user-variable input (e.g., [0043], [0105]); and
simulating, by the at least one processor, operation of the equipment using the digital twin in response to receiving an entry in the at least one user-variable input (e.g., Fig. 1 #110, Fig. 12, [0043], [0058]-[0059]).
2. The method of claim 1, wherein the data is received from at least one of the equipment, one or more sensors, one or more energy meters, and one or more controllers (e.g., [0049], [0055]).
3. The method of claim 1, further comprising pre-processing, by the at least one processor, the data before the training (e.g., [0057], [0102]-[104]).
4. The method of claim 3, wherein the pre-processing comprises at least one of:
identifying one or more zero values;
converting at least a portion of the data to a numeric format;
removing at least one data column with a missing data count above a threshold;
filling in at least one missing data entry with dummy data (e.g., [0103]);
performing one-hot encoding for categorical data; and
summing at least two data entries (e.g., [0057], [0102]-[0104]).
6. The method of claim 1, further comprising evaluating, by the at least one processor, performance of the ML model before the building of the model accessible by the UI (e.g., [0058]-[0059]).
7. The method of claim 6, wherein the evaluating comprises at least one of:
using a test dataset to run the trained ML model;
calculating mean squared error of the trained ML model;
calculating an R-squared score for the trained ML model;
calculating mean absolute error of the trained ML model; and
evaluating feature importance for features within the data (e.g., [0043]).
8. The method of claim 1, wherein the building of the model accessible by the UI comprises building a 3D model of the equipment (e.g., [0094]).
9. The method of claim 1, wherein the simulating comprises running the trained ML model with the entry as an input (e.g., Fig. 1 #110, Fig. 12, [0043]).
10. The method of claim 1, wherein the simulating comprises at least one of updating the UI with results of the simulating (e.g., Fig. 14) and adjusting an operational input to the equipment based on the results of the simulating (e.g., Fig. 13).
11. A system comprising:
at least one processor (e.g., Fig. 2); and
at least one non-transitory computer readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform processing (e.g., Fig. 2) comprising:
storing, by the at least one processor, data descriptive of operation of equipment in a data store (e.g., [0049], [0055]);
building a digital twin of the equipment using the data (e.g., Fig. 1 #110, [0049], [0055]), the building comprising:
training at least one machine learning (ML) model on at least a portion of the data in the data store (e.g., Fig. 1 #125, “Training Process”); and
building a model accessible by a user interface (UI) and comprising at least one parameter determined by the trained ML model (e.g., Fig. 1 #125, [0058]-[0059]), wherein the model includes at least one user-variable input (e.g., [0043], [0105]); and
simulating operation of the equipment using the digital twin in response to receiving an entry in the at least one user-variable input (e.g., Fig. 1 #110, Fig. 12, [0043], [0058]-[0059]).
12. The system of claim 11, wherein the data is received from at least one of the equipment, one or more sensors, one or more energy meters, and one or more controllers (e.g., [0049], [0055]).
13. The system of claim 11, wherein the processing further comprises pre-processing the data before the training (e.g., [0057], [0102]-[104]).
14. The system of claim 13, wherein the pre-processing comprises at least one of:
identifying one or more zero values;
converting at least a portion of the data to a numeric format;
removing at least one data column with a missing data count above a threshold;
filling in at least one missing data entry with dummy data (e.g., [0103]);
performing one-hot encoding for categorical data; and
summing at least two data entries (e.g., [0057], [0102]-[0104]).
16. The system of claim 11, wherein the processing further comprises evaluating performance of the ML model before the building of the model accessible by the UI (e.g., [0058]-[0059]).
17. The system of claim 16, wherein the evaluating comprises at least one of:
using a test dataset to run the trained ML model;
calculating mean squared error of the trained ML model;
calculating an R-squared score for the trained ML model;
calculating mean absolute error of the trained ML model; and
evaluating feature importance for features within the data (e.g., [0043]).
18. The system of claim 11, wherein the building of the model accessible by the UI comprises building a 3D model of the equipment (e.g., [0094]).
19. The system of claim 11, wherein the simulating comprises running the trained ML model with the entry as an input (e.g., Fig. 1 #110, Fig. 12, [0043]).
20. The system of claim 11, wherein the simulating comprises at least one of updating the UI with results of the simulating (e.g., Fig. 14) and adjusting an operational input to the equipment based on the results of the simulating (e.g., Fig. 13).
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 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Harvey et al. in view of Ng et al. US 2024/0329626 (“Ng”).
Harvey discloses that the ML model comprises regression (e.g., [0049]), but does not disclose that the ML model comprises a random forest regression model, as recited in claims 5 and 15.
Ng (in combination with Harvey) discloses:
5. The method of claim 1, wherein the ML model comprises a random forest regression model (e.g., [0060]).
15. The system of claim 11, wherein the ML model comprises a random forest regression model (e.g., [0060]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Harvey with Ng since Ng teaches that random forest can be used to effectively to train a machine learning model (e.g., [0060]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN A JARRETT whose telephone number is (571)272-3742. The examiner can normally be reached M-F 9:00-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at 571-272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN A JARRETT/Primary Examiner, Art Unit 2116
10/07/26
