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 .
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-6, and the embodiment of Fig. 1 in the reply filed on 08/11/2025 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
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 for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 & 3-6 do not clearly set forth the metes and bounds of the patent protection desired. Claim 1 is vague and unclear reciting “[...] to detect an abnormal situation of the sensor data, and raising an alarm if the abnormal situation occurs; [...] to diagnose the detected abnormal situation, so as to determine a type of abnormal situation; and [...] to optimize and control parameters of the sewage treatment process based on the deep learning technology” because the claim appears to define the results to be achieved without clear method steps. Claim 3 is similarly vague and unclear reciting i.e., [...] by adopting a Legendre deep network model; [...] a residual generator; [...] wherein the Legendre deep network model adopts a learning algorithm for learning, and the learning algorithm is selected from a group consisting of a BP learning algorithm, an RLS learning algorithm, and an L-M learning algorithm; [...] to diagnose the detected abnormal situation, so as to determine [...] to optimize and control [...] to obtain an optimal set value of a control variable; tracking the set value with a controller to optimize and control the sensor data in the sewage treatment process.” Claims 4-6 are similarly unclear. A claim is indefinite where it merely recites a use without any active, positive steps delimiting how this use is actually practiced.
Regarding claim 1, the phrase "if" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention.
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.
Claim(s) 1, 2, 5, 6 is/are rejected under 35 U.S.C. 102a1/a2 as being anticipated by Cella et al. (WO 2022/072921).
Regarding claim 1, Cella et al. teach:
1. A remote monitoring method for a sewage treatment process (¶ 1665+), comprising steps of:
collecting sensor data with a sewage treatment data collection platform, wherein the sewage treatment data collection platform has at least one sensor for collecting sewage data (see ¶ 0006, 1390-1391+ for example);
establishing an abnormal situation detection platform with a deep learning technology to detect an abnormal situation of the sensor data, and raising an alarm if the abnormal situation occurs (see i.e., alarm, alert, warnings, display colors associated with fault level severity, ¶ 0779, 4462, 4465, 4537+);
establishing an abnormal situation diagnosis platform with the deep learning technology to diagnose the detected abnormal situation (see ¶ 0684, 0977+ for example), so as to determine a type of abnormal situation; and
using the sensor data to optimize and control parameters of the sewage treatment process based on the deep learning technology (see ¶ 0684, 4531+ for example).
Regarding claims 2, 5, 6, Cella et al. teach:
2. The remote monitoring method, as recited in claim 1, wherein the sensor of the sewage treatment data collection platform comprises a temperature sensor, an acidity meter, an alkalinity meter, a flow meter, a camera, and a millimeter-wave radar (see ¶ 0006, 1390-1391+ for example).
5. The remote monitoring method, as recited in claim 1, further comprising a step of: pre-processing the sensor data to remove noise after collecting the sensor data by the sewage treatment data collection platform and before establishing the abnormal situation detection platform with the deep learning technology to detect the abnormal situation of the sensor data (see ¶ 0054 for example).
6. The remote monitoring method, as recited in claim 5, wherein pre-processing the sensor data to remove the noise comprises specific steps of: performing data storage and data pre-processing through a cloud server, wherein the data pre-processing comprises: decomposing the sensor data, removing a part of high-frequency components, and reorganizing the sensor data for de-noising (see ¶ 0065, 0631, 0736 for example).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (WO 2022/072921).
Regarding claims 3-4, Cella et al. teach the machine learning model utilizing a plurality of layers of nodes (¶ 4525), and statistical data processing (¶ 1908-1910+, 1799). However, Cella et al. do not explicitly teach the claimed method of a Legendre deep network model. It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the methods of Cella et al. with a Legendre deep network model as claimed for a plant operation optimization. The Court in KSR, “[w]hen a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one”, 550 U.S. at ___, 82 USPQ2d at 1396 (emphasis added), or solves a problem which is different from that which the applicant was trying to solve, may also be considered for the purposes of 35 U.S.C. 103. See MPEP 2141. Therefore, although the specific claimed elements are not taught, selecting appropriate model (i.e., LDNN) for the design of a plant operation optimization would have been obvious to one of ordinary skill in the art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEAN KWAK whose telephone number is (571)270-7072. The examiner can normally be reached M-TH, 4:30 am - 2:30 pm EST.
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/DEAN KWAK/Primary Examiner, Art Unit 1798
DEAN KWAK
Primary Examiner
Art Unit 1798