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 .
This Action is non-final and is in response to the claims filed April 23, 2026 via RCE. Claims 1, 3, 5-7, and 9-15 are currently pending, of which claims 1, 3, and 7 are currently amended. Claims 2, 4, and 8 were previously cancelled and claims 13-15 are newly presented.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 23, 2026 has been entered.
Response to Arguments
Rejections Under 35 U.S.C. §112
Applicant has amended the claims at issue and the previous rejection has therefore been withdrawn.
Prior Art Rejections
Applicant has amended the claims at issue and argues that the various claimed features are not taught by the previously cited art. Specifically, Applicant has removed some of the previous potential quantities of interest to select to be measured and compared and argues the new list is not taught by the previously cited references. See Remarks 10. These arguments are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Specifically, new reference Immer discloses various parameters that would apply to the analysis of Li. See Immer paras. [0024-25], [0046] and [0082]. Immer has also replaced McLellan and Dugue throughout the claim rejections.
It is for at least these reasons, and the reasons cited below, that the claims remain rejected in this Action.
Claim Objections
Claims 1 and 7 are objected to for the following informalities:
Claim 1 recites “lime black spill area” and this appears to be a typographical error and should read “back spill area”. See Specification para. [0026]. Claim 7 recites similar language and is objected to for at least the same reasons therein.
Appropriate correction is required.
Examiner’s Note
The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
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.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 3, 5-7, and 9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (“Multisource Data Ensemble Modeling for Clinker Free Lime Content Estimate in Rotary Kiln Sintering Processes”; retrieved from IDS filed July 7, 2023; hereinafter “Li”), and further in view of Immer et al. (U.S. Publication No. 2015/0316262; hereinafter, “Immer”)
As per claim 1, Li further teaches a method for monitoring a flame of a burner of a lime kiln, comprising:
imaging [a video stream] showing the burner end of the lime kiln to generate an imaged video stream (See Li Figs. 1 and 3 and pp. 304 “extract the color and configuration features of flame image ROI to identify the burning state”);
extracting at least one image from the imaged video stream (See Li Figs. 1 and 3 and pp. 304 “extract the color and configuration features of flame image ROI to identify the burning state”);
determining, using a pretrained algorithm, from the at least one image at least one area of interest (See Li p. 306: “Training flame image ROI are preprocessed by a compact Gabor filter bank to distinguish ROI. Then, color feature fa, global configuration feature fb, and local configuration feature fc of the ROI are extracted”; III.A.1: “Motivated by the knowledge that discriminative ROI facilitate feature extraction and ROI with distinct texture attributes, the Gabor filter emerged as the most popular texture analysis method, and hence is employed to discriminate ROI”), wherein the at least one area of interest comprises a part of the at least one image showing an area comprising at least one characteristic portion of the flame and/or burner end (See Li p. 306, Fig. 4 and Section III.A.1: ROI associated with flame image and various texture attributes);
calculating an area, a location or a feature of the at least one characteristic portion based on pixels in the at least one area of interest (See Li Fig. 4 and p. 306 Section III.A.1: “two fixed windows, 25 × 25 pixels in size, are used to sample the ROI to represent their texture attributes as shown in Fig. 4; p. 307 III.B.1: “the area feature fa of the training and testing flame images can be calculated to feature the color of the ROI”);
determining a value for the selected quantity of interest based on the calculated area, the location or the feature of the at least one characteristic portion (See Li p. 311 Section V.B: “Following the procedure in Section III-B, via the fixed masking matrix M, the area feature f a of the flame images can be extracted to represent the color of the ROI, whilst according to (10), the number of selected eigen-flame images, i.e., the dimension of the extracted ROIs global configuration feature f b, can be chosen for each replica”).
However, while Li teaches multiple images, Li does not explicitly teach a video stream of the flame in the kiln.
Immer teaches capturing flame images using video streams (See Immer para. [0064]: burner data collected via video recording).
Furthermore, while Li selects and compares flame information and images, Li does not explicitly teach a variety of characteristics.
Immer teaches selecting a quantity of interest from at least one of: a lime area, a flame pumping index, a dust index inside the lime kiln, or a lime black spill area (See Immer paras. [0024-25], [0046] and [0082]: various rates for operational parameters such as burner integrity, flame stability, flame position, and also calculate values such as flowrates, firing rates, viscosity estimates, burner stoichiometries).
Additionally, while Li teaches the calculated areas and quantity of interest, Li does not compare those to threshold values.
Immer further teaches comparing the value of the selected quantity of interest to a threshold value (See Immer paras. [0022] and [0080]: comparative analysis of to set points and alarm/alert threshold values. This would be done using the calculations and determinations of Li).
Moreover, while Li compares images, Li does not adjust burner operations based on image comparisons.
Immer teaches adjusting the operation of the burner based on the comparison of the value of the selected quantity of interest to the threshold value (See Immer paras. [0005], [0023], and [0034]: tuning local flame characteristics, where control can be performed by control valves or flow controls. This would be in response to the comparative analysis).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the images of the kiln of Li with the specific measurements of Immer. One would have been motivated to combine these references because both references disclose capturing burner images to determine flame features. Immer enhances the image comparison and capturing of Li by expanding upon the amount of data that can be gathered, allowing users/operators to determine more about the flame ROI at issue and allow for more accurate potential adjustments. This, along with the adjustments, “can provide valuable information that enables an operator to perform preventive maintenance only when needed and to avoid costly unexpected failures or shutdowns” (See Immer para. [0063]).
As per claim 3, Li teaches the method according to claim 1. However, Li does not display user interface elements.
Immer further teaches displaying the selected quantity of interest and the value of the selected on a user interface element (See Immer paras. [0072-73] and [0081-83]: display interface for information, including current data and trends, as well as status of burner/sensors and the associated alerts/alarms).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Li with the teachings of Immer for at least the same reasons as discussed above in claim 1.
As per claim 5, Li/Immer teaches the method according to claim 1. Li further teaches wherein the pretrained algorithm is created by imaging the [viuo stream] showing the burner end of the lime kiln; extracting a plurality of training images from the imaged [video stream] and segmenting each image of the plurality of training images into areas of interest, wherein the area of interest for at least one image comprises the interior of the lime kiln; and training an algorithm to recognize the areas of interest from the segmented images (See Li p. 306 Section II.B: training using ROI that is captured from burner; III.A.1: “Motivated by the knowledge that discriminative ROI facilitate feature extraction and ROI with distinct texture attributes, the Gabor filter emerged as the most popular texture analysis method, and hence is employed to discriminate ROI”; p. 307 III.B.1: “as the burning state changes, the pixel locations in such a plot change significantly. This enables one to use masking to obtain more meaningful ROI to feature various clinkers. According to [24], a 256×256 binary masking matrix M with fixed graphics is constructed, also as shown in Fig. 5(b). Then, the area feature fa of the training and testing flame images can be calculated to feature the color of the ROI”).
However, while Li teaches multiple images, Li does not explicitly teach a video stream of the flame in the kiln.
Immer teaches capturing flame images using video streams (See Immer para. [0064]: burner data collected via video recording).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Li with the teachings of Immer for at least the same reasons as discussed above in claim 1.
As per claim 6, Li/Immer teaches the method according to claim 5. Li further teaches extracting a reference image from the imaged [video stream] (See Li Figs. 1 and 3 and pp. 304 “extract the color and configuration features of flame image ROI to identify the burning state”); and determining using known dimensions of the lime kiln visible in the reference image corresponding size in Si-units for a pixel of the reference image (See Li p. 306 Section III.A.1: “two fixed windows, 25 x 25 pixels in size, are used to sample the ROI to represent their texture attributes as shown in Fig. 4”; Section II.B: training using ROI processing and thus based on the pixel windows).
However, while Li teaches multiple images, Li does not explicitly teach a video stream of the flame in the kiln.
Immer teaches capturing flame images using video streams (See Immer para. [0064]: burner data collected via video recording).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Li with the teachings of Immer for at least the same reasons as discussed above in claim 1.
As per claim 11, Li/Immer further teaches the method according to claim 1. However, while Li teaches closed-loop control strategies, Li does not teach specific adjustments to the flame as they relate to air distribution or fuel flow.
Immer teaches wherein the adjusting the operation of the burner includes adjusting distribution of air entering the burner or adjusting an amount of fuel flowing into the burner (See Immer paras. [0005], [0023], and [0034]: tuning local flame characteristics, where control can be performed by control valves or flow controls).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Li with the teachings of Immer for at least the same reasons as discussed above in claim 1.
As per claims 7 and 12, the claims are directed to a system that implements the method of claims 1 and 11, respectively, and are therefore rejected for at least the same reasons therein. Furthermore, Immer teaches a processor configured to implement said method (See Immer paras. [0069-71]).
As per claim 9, the claim is directed to a computer program product that implements the method of claim 1 and is rejected for at least the same reasons therein. Furthermore, Immer teaches a computer program product to implement said method (See Immer paras. [0069-71]).
As per claim 10, the claim is directed to a memory medium that implements the program product of claim 9 and is rejected for at least the same reasons therein. Furthermore, Immer teaches a memory medium to implement said program product (See Immer paras. [0069-71]).
As per claim 14, Li/Immer further teaches the method according to claim 1, wherein the determining of the value for the selected quantity of interest based on the feature includes determining a color property of the at least one characteristic portion (See Li pp. 304 Section I. and 307 Section III.B: “Flame Image Color Feature” where the color of the flame is determined in the ROI).
Claim 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li/Immer as applied above, and further in view of Coates et al. (U.S. Publication No. 2010/0294700; hereinafter “Coates”).
As per claim 13, Li/Immer teaches the method according to claim 1. While Li/Immer teaches determining of the value for the selected quantity of interest, as well as the calculated area, Li/Immer does not teach the cross-sectional area of the kiln in the calculations.
Coates teaches wherein this determining is based on the calculated area and the value represents a proportion of a cross-sectional area of the kiln corresponding to the calculated area (See Coates paras. [0014-16] and [0049]: cross sectional analysis of a kiln, included the projected location of the bed of material within the kiln).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the measurements and calculations of Li/Immer with the cross sections of Coates. One would have been motivated to combine these references because both references disclose monitoring and optimizing kiln performance, and Coates further enhances the measurements of Li/Immer by optimizing “the heat transfer to the initial material while also providing a means of maintaining the pre-selected wall temperature of the kiln” (See Coates paras. [0012-13]).
Claim 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li, and further in view of Immer and Coates.
As per claim 15, Li teaches a method for monitoring a flame of a burner of a lime kiln, comprising:
imaging [a video stream] showing the burner end of the lime kiln to generate an imaged [video stream] (See Li Figs. 1 and 3 and pp. 304 “extract the color and configuration features of flame image ROI to identify the burning state”);
extracting at least one image from the imaged [video stream] (See Li Figs. 1 and 3 and pp. 304 “extract the color and configuration features of flame image ROI to identify the burning state”);
determining, using a pretrained algorithm, an area of interest in the at least one image, wherein the area of interest is either an area of a white flame or a black flame of a burner flame shown in the at least one image (See Li p. 306: “Training flame image ROI are preprocessed by a compact Gabor filter bank to distinguish ROI. Then, color feature fa, global configuration feature fb, and local configuration feature fc of the ROI are extracted”; III.A.1: “Motivated by the knowledge that discriminative ROI facilitate feature extraction and ROI with distinct texture attributes, the Gabor filter emerged as the most popular texture analysis method, and hence is employed to discriminate ROI”; p. 306, Fig. 4 and Section III.A.1: ROI associated with flame image and various texture attributes, including flame color. As shown in the colored Figs. 1 and 4, there are black areas and bright white areas captured from the flame).
However, while Li teaches multiple images, Li does not explicitly teach a video stream of the flame in the kiln.
Immer teaches capturing flame images using video streams (See Immer para. [0064]: burner data collected via video recording).
Additionally, while Li teaches the calculated areas and quantity of interest, Li does not compare those to threshold values.
Immer further teaches comparing the value to a threshold value (See Immer paras. [0022] and [0080]: comparative analysis of to set points and alarm/alert threshold values. This would be done using the calculations and determinations of Li).
Moreover, while Li compares images, Li does not adjust burner operations based on image comparisons.
Immer teaches adjusting the operation of the burner based on the comparison of the value of the selected quantity of interest to the threshold value (See Immer paras. [0005], [0023], and [0034]: tuning local flame characteristics, where control can be performed by control valves or flow controls. This would be in response to the comparative analysis).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the images of the kiln of Li with the thresholds and adjustments of Immer. One would have been motivated to combine these references because both references disclose capturing burner images to determine flame features. Immer enhances the image comparison analytics of Li by “provid[ing] valuable information that enables an operator to perform preventive maintenance only when needed and to avoid costly unexpected failures or shutdowns” (See Immer para. [0063]).
However, while Li/Immer teaches determining of the value for the selected quantity of interest, Li/Immer does not teach the cross-sectional area of the kiln in the calculations.
Coates teaches determining a value representing a proportion of a cross-sectional area of the kiln corresponding to the area of interest (See Coates paras. [0014-16] and [0049]: cross sectional analysis of a kiln, included the projected location of the bed of material within the kiln).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the measurements and calculations of Li/Immer with the cross sections of Coates. One would have been motivated to combine these references because both references disclose monitoring and optimizing kiln performance, and Coates further enhances the measurements of Li/Immer by optimizing “the heat transfer to the initial material while also providing a means of maintaining the pre-selected wall temperature of the kiln” (See Coates paras. [0012-13]).
Conclusion
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/NICHOLAS KLICOS/Primary Examiner, Art Unit 2118
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