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 .
IDS
The information disclosure statements (IDS) submitted on July 22, 2024, and October 2, 2025, are being considered by the Examiner.
Drawing
The drawing filed on July 22, 2024, is accepted by the Examiner.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objection
Claims 1-6 are objected to because of the following minor informalities: the step designation in claims 1 and 4 seem to have redundant label, for instance since the steps in Fig. 1 are identified as a-f or s100 to s600, it should be labeled as step s100 to step s600, similarly regarding claim 4, the steps are labeled as s510 to s540, and no need to label them as e-1 to e-4 and f as s600. Note that simply amending the steps as step s100, …. s600, and s510…. s540 would be a better labeling for claims 1 and 4 respectively. The remaining claims are objected to because of their dependency on claim 1 of the instant application.
Claim rejection – 35 U.S.C. §112
Claims 1-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite: in reference to claim 1, in step b, the claim calls for “the acquired data” and yet in step a, there are two “input and output data” are noted, one being “to acquire input and output data” and the second being “input output data concerning exhaust gas concentrations”. Therefore this “input and output” data should be clearly defined to maintain proper antecedent basis for the limitation in the claim. The remaining claims depend on claim 1 and inherit the attributes of the base claim.
Claim 4 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all the limitations of the base claim and any intervening claims.
None of the references under consideration discloses the steps noted in s510 through s540.
Claim rejection – 35 U.S.C. §102
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 names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 5-6 are rejected under 35 U.S.C. §102 (a)(2) as being anticipated by Roverso (U.S. PAP 2011/0010318, hereon Roverso).
In reference to claim 1: Roverso discloses an industrial boiler monitoring system for artificial intelligence-based exhaust gas analysis and fault diagnosis (see Roverso, Abstract, paragraph [0006]- [0007]) comprising:
Step S100: a service provider operates individual boilers of a customer for a predetermined period after test-driving of the individual boilers to acquire input and output data concerning essential components and input and output data concerning measured exhaust gas concentrations (see Roverso, paragraph [0043]).
Step S200 creating big data for the individual boilers of the customer based on the acquired data (see Roverso, paragraph [0058] where input/output values are established based empirical models);
Step S300 constructing a virtual sensor module for measuring the concentration of virtual exhaust gas and a fault diagnosis module for diagnosing faults of components through machine learning techniques based on the big data (see Roverso, paragraph [0101] to [0102]);
Step S400: installing the virtual sensor module and the fault diagnosis module constructed in step (c) on the individual boilers, and transmitting operational data generated during the operation of the individual boilers to a management server (see Roverso, paragraph [0086]);
Step S500) calculating the exhaust gas concentration using the operational data by the virtual sensor module in the individual boilers (see Roverso, paragraph [0103]), and calculating the failure potential of components by the fault diagnosis module (see Roverso, Fig. 6, absolute error); and
Step S600: the individual boilers transmit and store the calculated concentrations of exhaust gas and the results of fault diagnosis of the components to the management server via application and web, and in which the service provider monitors the combustion state of the individual boilers in real-time and issues an alarm at the time of abnormal operation of the boilers caused by incomplete combustion or component failures (see Roverso, paragraphs [0005], [0044], and [0048]).
Regarding claim 2: Roverso further discloses that the fault diagnosis module is constructed by a principal component analysis (PCA) model using the machine learning technique (see Roverso, paragraph [0039]).
Regarding claim 3: Roverso further discloses that the fault diagnosis by the fault diagnosis module is conducted by the reconstruction-based contribution (RBC) technique based on error instruction correction, which is based on real-time measurements of temperature, pressure, and flow rates from the sensors installed on the components, and predicted values learned through the PCA model (see Roverso, paragraph [0039]).
Regarding claim 5: Roverso further discloses that the virtual sensor module is constructed by a regression model utilizing the statistical-based partial least squares (PLS) and the auto-encoder neural network among the machine learning techniques (see Roverso, paragraphs [0029] and [0032]).
Regarding claim 6: Roverso further discloses that the virtual sensor module predicts concentrations of oxygen (O2), sulfur oxides (SOx), nitrogen oxides (NOx), and carbon monoxide (CO) using the operational data of the individual boil (see Roverso, paragraph [0088]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kwon et al. (U.S. Patent No. 12,693,009) discloses an AI-based air damper control system and method for industrial boilers. An AI-based optimal air damper control method according to an embodiment calculates energy efficiency under a given control condition and an environment by extracting energy efficiency-related data from industrial boiler operational data and analyzing a correlation between corresponding data, trains an AI-based optimal air volume-for-load prediction model by using the extracted data and the calculated energy efficiency as training data, and derives an air volume condition that results in peak energy efficiency under a given load, based on the trained optimal air volume-for-load prediction model, and automatically controls the air damper according to the corresponding air volume condition.
Keeler et al. (U.S. Patent No. 5,386,373) discloses a continuous emission monitoring system for a manufacturing plant includes a control system which has associated therewith a virtual sensor network. The network is a predictive network that receives as inputs both control values to the plant and sensor values. The network is then operable to map the inputs through a stored representation of the plant to output a predicted pollutant sensor level.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIAS DESTA whose telephone number is (571)272-2214. The examiner can normally be reached M-F: 8:30 to 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicants are encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew M Schechter can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ELIAS DESTA/
Primary Examiner, Art Unit 2857
Prosecution Insights