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 .
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)(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.
(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 – 20 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Maturana et al. US 2015/027620 (hereinafter Maturana).
Regarding claim 1, Maturana teaches: a system, comprising:
processing circuitry; and
a memory, accessible by the processing circuitry, the memory storing instructions that, when executed by the processing circuitry, cause the processing circuitry to perform operations comprising:
receiving operational data captured from an industrial automation system performing an industrial automation process (Fig. 15, [0102] - - receiving industrial data);
modeling the industrial automation process based on the operational data (Fig. 15, [0102] - - simulating boiler system using initial conditions);
modeling one or more adjustments to the industrial automation process (Fig. 15, [0102] - - modify conditions based on tunning rules);
identifying that the modeling of the one or more adjustments to the industrial automation process indicates that the one or more adjustments to the industrial automation process improve one or more sustainability metrics for the industrial automation process (Fig. 15, [0102] - - repeat until maximum efficiency; efficiency is a metric);
generating one or more sustainability recommendations to implement the one or more adjustments to the industrial automation process (Fig. 15, [0102] - - resulting set point parameters is recommended); and
implementing the one or more adjustments to the industrial automation process (Fig. 15, [0102] - - apply the set point parameters to boiler system).
Regarding claim 2, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: receiving training data captured from a plurality of industrial automation systems performing a plurality of industrial automation processes ([0058], [0066] - - modeling using an incremental learning system, thus the boiler model is trained using historical data);
building a training dataset based on the training data ([0058], [0066] - - historical data is training data); and
training a model using the training dataset, wherein the model is configured to model the plurality of industrial automation processes ([0058], [0066] - - modeling using an incremental learning system, thus the boiler model is trained using historical data) .
Regarding claim 3, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: receiving additional training data captured from the plurality of industrial automation systems performing the plurality of industrial automation processes;
building a supplemental training dataset based on the additional training data; and
retraining the model using the supplemental training dataset ([0058] - - incremental learning system; iteratively update the boiler behavioral model over time as new data is gathered from the boiler system).
Regarding claim 4, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: receiving a trained model from a service provider;
wherein modelling the industrial automation process based on the operational data comprises applying the trained model to the operational data (Fig. 4, [0050], [0051] - - the analytic component receives a model generated by modeling component; the analytic component applies the model to the industrial data to perform analysis).
Regarding claim 5, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: displaying, via a graphical user interface, the one or more sustainability recommendations; and
receiving an input authorizing the one or more sustainability recommendations ([0045], [0056] - - present set point parameters to a user; the user choose to apply the recommended set point parameters).
Regarding claim 6, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: the sustainability metrics comprise energy consumption, emissions, water consumption, raw material consumption, chemical usage, waste produced, carbon footprint, or any combination thereof ([0064] - - optimal efficiency, fuel consumption).
Regarding claim 7, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: the one or more sustainability recommendations comprise adjusting one or more operational parameters, adjusting one or more threshold values, adjusting a production schedule, scheduling of maintenance, scheduling a service, or any combination thereof (Abstract - - set point parameters are operation parameters).
Regarding claim 8, Maturana teaches: a method, comprising:
receiving first operational data representative of a plurality of respective industrial automation processes performed by a plurality of industrial automation systems (Fig. 15, [0102] - - receiving industrial data with multiple boilers);
training a model to simulate the plurality of respective industrial automation processes based on the first operational data (Fig. 15, [0102] - - boiler model; [0058], [0066] - - modeling using an incremental learning system, thus the boiler model is trained using historical data);
receiving second operational data captured from a particular industrial automation system performing a particular industrial automation process at a particular facility (Fig. 15, [0102] - - modify conditions based on tunning rules);
applying the trained model to simulate the particular industrial automation process based on the second operational data (Fig. 15, [0102] - - simulating boiler system using modified conditions);
identifying that simulating one or more adjustments to the particular industrial automation process indicates that the one or more adjustments to the particular industrial automation process improve one or more sustainability metrics for the particular industrial automation process (Fig. 15, [0102] - - repeat until maximum efficiency; efficiency is a metric);
generating one or more sustainability recommendations to implement the one or more adjustments to the particular industrial automation process (Fig. 15, [0102] - - resulting set point parameters is recommended); and
transmitting the one or more sustainability recommendations to the particular facility (Fig. 15, [0102] - - apply the set point parameters to boiler system).
Regarding claim 9, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: the one or more adjustments are generated by the trained model (Fig. 15, [0102] - - resulting set point parameters is recommended).
Regarding claim 10, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: the one or more adjustments are generated based on the first operational data representative of the plurality of respective industrial automation processes performed by the plurality of industrial automation systems ([0102] - - modify initial condition based on tunning rules, the tunning rule specify how each initial condition should be adjusted, thus the modified condition is based on initial condition which is the first operational data).
Regarding claim 11, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: receiving third operational data representative of the plurality of respective industrial automation processes performed by the plurality of industrial automation systems; and
retraining the trained model to simulate the plurality of respective industrial automation process based on the first operational data and the third operational data ([0058] - - incremental learning system; iteratively update the boiler behavioral model over time as new data is gathered from the boiler system).
Regarding claim 12, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: generating one or more visualizations illustrating how the one or more adjustments to the particular industrial automation process improve the one or more sustainability metrics for the particular industrial automation process ([0045] - - display results of analyses; [0063] - - graphical reports).
Regarding claim 13, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: the sustainability metrics comprise energy consumption, emissions, water consumption, raw material consumption, chemical usage, waste produced, carbon footprint, or any combination thereof ([0064] - - optimal efficiency, fuel consumption).
Regarding claim 14, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: the one or more sustainability recommendations comprise adjusting one or more operational parameters, adjusting one or more threshold values, adjusting a production schedule, scheduling of maintenance, scheduling a service, or any combination thereof (Abstract - - set point parameters are operation parameters).
Regarding claim 15, Maturana teaches: a non-transitory computer readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations comprising:
collecting operational data from an industrial automation system performing an industrial automation process (Fig. 15, [0102] - - receiving industrial data);
transmitting the operational data to a service provider (Fig. 15, [0102] - - cloud platform is a service provider; cloud platform receiving industrial data);
receiving, from the service provider, one or more sustainability recommendations to implement one or more adjustments to the industrial automation process, wherein modeling of the one or more adjustments to the industrial automation process indicates that the one or more adjustments to the industrial automation process improve one or more sustainability metrics for the industrial automation process (Fig. 15, [0102] - - simulating boiler system using conditions; modify conditions based on tunning rules; repeat until maximum efficiency; efficiency is a metric); and
implementing the one or more adjustments to the industrial automation process (Fig. 15, [0102] - - apply the set point parameters to boiler system).
Regarding claim 16, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: the operations comprise processing the collected operational data to filter the operational data, remove one or more data points from the operational data, or both, before transmitting the operational data to the service provider ([0078] - - filter the data according to specified filtering criterion).
Regarding claim 17, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: displaying, via a graphical user interface, the one or more sustainability recommendations; and
receiving an input authorizing the one or more sustainability recommendations ([0045], [0056] - - present set point parameters to a user; the user choose to apply the recommended set point parameters).
Regarding claim 18, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: collecting additional operational data from the industrial automation system performing the industrial automation process;
transmitting the additional operational data to the service provider; and
receiving, from the service provider, one or more additional sustainability recommendations to implement one or more additional adjustments to the industrial automation process, wherein modeling of the one or more additional adjustments to the industrial automation process indicates that the one or more additional adjustments to the industrial automation process improve the one or more sustainability metrics for the industrial automation process ([0061] - - adjustment of the previously determined set point parameters is likely to yield improved system performance, efficiency; new set point parameters are determined).
Regarding claim 19, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: the operational data is transmitted to the service provider by an edge device (Fig. 2 - - on premise cloud agent is an edge device).
Regarding claim 20, Maturana teaches all the limitations of the base claims as outlined above.
Maturana further teaches: the sustainability metrics comprise energy consumption, emissions, water consumption, raw material consumption, chemical usage, waste produced, carbon footprint, or any combination thereof ([0064] - - optimal efficiency, fuel consumption).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUHUI R PAN whose telephone number is (571)272-9872. The examiner can normally be reached Monday-Friday 8AM-5PM EST.
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/YUHUI R PAN/Primary Examiner, Art Unit 2116