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. This communication is responsive to application filed on 01/04/2023. Claims 1-10 are presented for examination. Information Disclosure Statement The information disclosure statement (IDS) submitted on 01/04/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 ( Does this claim fall within at least one statutory category? ): Claims 1-7 are directed to a method. Claim 8 is directed to a system. Claim 9 is a system. Claim 10 is a product. Therefore, claims 1-10 fall into at least one of the four statutory categories. Step 2A, Prong 1: ( (a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2) ): Claim 1: An online model water quality conversion method, comprising: determining a type of online real-time data [“ mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion )]; establishing a conversion formula for calculation data and the online real-time data [“ mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion ) and/or mathematical concepts ]; acquiring water quality data over the years, determining conversion- related parameters of the conversion formula, and establishing a water quality data conversion model [“ mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion )]; and substituting the online real-time data obtained by real-time measurement into the water quality data conversion model [“ mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion )], and performing real-time conversion to obtain the calculation data [“ mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion ) and/or mathematical concept ]. Step 2A, Prong 2 ( 1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application ): The claim is directed to the judicial exception. Claim 1 has no additional limitations that integrate the abstract idea into a practical application. Step 2B: ( Does the claim recite additional elements that amount to significantly more than the judicial exception? No): Claim 1 has no additional limitations that integrate the abstract idea into an inventive concept. As per claim 2, the claim falls into [insignificant extra solution, e.g. mere data-gathering]. As per claim 3, the claim falls into [insignificant extra solution, e.g. mere data-gathering]. As per claim 4 , the claim falls into [insignificant extra solution, e.g. mere data-gathering]. As per claim 5 , the claim falls into [insignificant extra solution, e.g. mere data-gathering]. As per claim 6 , the claim falls into [insignificant extra solution, e.g. mere data-gathering]. As per claim 7 , the claim falls into [insignificant extra solution, e.g. mere data-gathering]. As per claim 8 , the claim falls into [insignificant extra solution, e.g. mere data-gathering]. As per claim 8 , the claim falls into [insignificant extra solution, e.g. mere data-gathering]. As per claim 8 , the claim falls into [insignificant extra solution, e.g. mere data-gathering]. As per claim 8 , the claim falls into [insignificant extra solution, e.g. mere data-gathering]. 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim recite s “computer readable storage medium”. Applicant specification describes “computer readable storage medium” as : “The above-mentioned computer-readable storage medium includes, but is not limited to: an optical storage medium (e.g. CD-ROM and DVD), a magneto-optical storage medium (e.g. MO), a magnetic storage medium (e.g. a magnetic tape or a removable hard disk), a medium with a built-in rewritable non-volatile memory (e.g. a memory card) and a medium with a built-in ROM (e.g. a ROM cassette)”. Based on this description, the computer readable storage medium claimed appears to cover both transitory and non-transitory embodiments. Transitory embodiments are not statutory under 35 USC 101. According to the current guidance, a proper medium that qualifies as a patent eligible process under 35 USC 101 must be non- transitory storage medium that is also a recording medium and should not include propagation media. Because the instant claims include medium that could involve propagation media, the claims are being held as non-statutory under 35 USC 101. 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 . Claim s 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al (Y. Cai, X. Fu, X. Gao, L. Li, Research progress of on-line automatic monitoring of chemical oxygen demand (COD) of water, pgs. 1-14, 2018) in view of US Publication No. 2012/0179373 issued to Lee et al . Claim 1. Cai et al discloses a n online model water quality conversion method, comprising: determining a type of online real-time data (See: pg. 8, first paragraph, The equipment could measure from 5 to 10 datum per second and it was adaptive for long and real time on-line monitoring of water parameter ; pg. 8 second paragraph, COD on-line monitoring system, which was developed by Zhao et al. [26], was based on UV visible spectroscopy and consisted of pulsed xenon lamp, concave holographic grating and spectral scanning structure. The wavelength range was 200~720 nm, and the COD concentration range was 30~1,000 mg/L. The system could not only measure the real-time COD, but also measure other real time water quality parameters such as chroma, turbidity and so on ) ; establishing a conversion formula for calculation data and the online real-time data (See: pg. 9, “3.5 Correlation coefficient method (TOC method)” The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater) ; determining conversion- related parameters of the conversion formula (See: pg. 9, “3.5 Correlation coefficient method (TOC method)”, The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater. The data showed that the linear regression equation could be established between the TOC and COD, and there was a significant correlation between TOC and COD. A linear regression equation between TOC and COD was established by Sun Liyan et al. [32] based on the correlation analysis of COD and TOC of organic pollutants done by 18 surface water quality automatic monitoring stations. For each of the 18 monitoring stations, regression studies were performed base on at least 20 sets of valid data to obtain the TOC-COD conversion curve. The slope range was 1.03~4.3, the intercept range was -7.38~23.78, and the correlation coefficient was 0.727~0.998, which showed that the correlation between TOC and COD was good) , and establishing a water quality data conversion model (See: pg. 9, “3.5 Correlation coefficient method (TOC method)”, The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater. The data showed that the linear regression equation could be established between the TOC and COD, and there was a significant correlation between TOC and COD. A linear regression equation between TOC and COD was established by Sun Liyan et al. [32] based on the correlation analysis of COD and TOC of organic pollutants done by 18 surface water quality automatic monitoring stations. For each of the 18 monitoring stations, regression studies were performed base on at least 20 sets of valid data to obtain the TOC-COD conversion curve) ; and substituting the online real-time data obtained by real-time measurement into the water quality data conversion model (See: pg. 9, “3.5 Correlation coefficient method (TOC method)”, The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater. The data showed that the linear regression equation could be established between the TOC and COD, and there was a significant correlation between TOC and COD. A linear regression equation between TOC and COD was established by Sun Liyan et al. [32] based on the correlation analysis of COD and TOC of organic pollutants done by 18 surface water quality automatic monitoring stations. For each of the 18 monitoring stations, regression studies were performed base on at least 20 sets of valid data to obtain the TOC-COD conversion curve ; pg. 9 last paragraph, The device combusts water samples in the combustion furnace under the temperature of 1,200 ℃ , uses the infrared analyzer to measure the amount of CO2 generated by combustion, calculates the TOC and then convert TOC to COD. The response time of the instrument is 1 minute and its measuring scope can reach 100~200,000 mg/L. ) , and performing real-time conversion to obtain the calculation data (See: Abstract, the on-line automatic monitoring of water quality is particularly urgent; pg. 9, “3.5. Correlation coefficient method (TOC method), The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater. The data showed that the linear regression equation could be established between the TOC and COD, and there was a significant correlation between TOC and COD. A linear regression equation between TOC and COD was established by Sun Liyan et al. [32] based on the correlation analysis of COD and TOC of organic pollutants done by 18 surface water quality automatic monitoring stations. For each of the 18 monitoring stations, regression studies were performed base on at least 20 sets of valid data to obtain the TOC-COD conversion curve. The slope range was 1.03~4.3, the intercept range was -7.38~23.78, and the correlation coefficient was 0.727~0.998, which showed that the correlation between TOC and COD was good ; pg. 11 “5. Conclusions”, with the improvement of embedded system and intelligent algorithms, on-line monitors will become more compact, integrated and intelligent. And the remote HMI and data sharing will make the real-time data display and remote operation control of the monitoring instrument much simpler. Besides, Water quality warning and sewage optimization options will become possible with the support of big data and cloud technology. “Monitoring problems-finding problems-early warning issues-optimizing solutions” can be achieved for the COD on-line monitoring equipment ) . Cia et al does not specify but Lee et al discloses acquiring water quality data over the years (See: par 0004] According to research, chemical water quality parameters have mutual correlation and thus, it is possible to measure another water quality parameter using the other chemical water quality parameters. Currently, research on a method of estimating an amount of nutrients such as total nitrogen, total phosphorus, and the like, using biochemical water quality parameters is being conducted. However, the level of research remains at a level that requires to correlate measurement values over a span of a few year s; par [0025] According to an exemplary embodiment of the present disclosure, by analyzing a plurality of multi-parameter water quality data and total phosphorus data that have been measured for a predetermined period (for example, for one to three year s) in a predetermined river, a correlation between the multi-parameter water quality and the total phosphorus is computed (S101), and an upper parameter having a high correlation as the computation result is selected (S103). In this instance, one to three parameters having the highest correlation among the above seven multi-parameter water quality may be selected as the upper parameter. In the present exemplary embodiment, it is assumed that three parameters are selected as the upper parameters). It would have been obvious before the effective filing date to combine technology of measuring total phosphorus using multi-parameters water quality as taught by Lee et al to on -li ne automatic monitoring of chemical oxygen demand of water of Cai et al would be to quickly handle the occurrence of water pollution ( Lee et al, par [0005] ). Claim 2. Cai et al discloses the online model water quality conversion method of claim 1, wherein the type of the online real-time data comprises COD, ammonia nitrogen, and a pH value (See: pg. 10, “3.6. Soft measurement method”, Soft measurement method uses sensors to collect basic parameters such as pH, dissolved oxygen, temperature, conductivity, turbidity, mixed liquid suspended solids, ammonia nitrogen, total carbon and others of water samples, and on the basis, establishes COD on-line nonlinear model through the algorithm ) . Claim 3.Cai et al discloses the online model water quality conversion method of claim 2, wherein the calculation data comprises soluble inert organic matters, easily degradable organic matters, particulate inert organic matters, slowly degradable organic matters, heterotrophic bacteria, autotrophic bacteria, microbial decay products, dissolved oxygen, nitrate nitrogen, ammonia nitrogen, easily biodegradable organic nitrogen, slowly biodegradable organic nitrogen, and alkalinity (See: pg. 4, “3.2.1 Electro-catalytic oxidation method”, electro-catalytic oxidation method is widely used in practice, and the use of PbO2 electrode oxidation method has a breakthrough progress in the field of electrochemical COD measurement method. As PbO2 electrode has high conductivity, high oxygen potential, and good inertia to strong acid and alkali, the hydroxyl radicals can be generated by the anode during electro-catalysis process to thoroughly degrade organic matters; pg. 8, “3.4 Biological method”, 3.4. Biological method Biological method measures COD by testing oxygen consumption during microbial decomposition after cultivating microorganism to decompose the organics in water. During the measurement process, firstly the biological matrix is acclimated and cultured by water samples to quickly degrade the organics in a special bioreactor. Secondly, the water sample in the bioreactor is subjected to aeration treatment until the dissolved oxygen reaches a saturated state, when the initial value of the dissolved oxygen concentration is measured. Then, the aeration is stopped, the water sample is injected into the bioreactor, and the organics in water sample are rapidly decomposed and dissolved oxygen is also consumed by the biological matrix). Claim 4. Cia et al discloses the online model water quality conversion method of claim 3, wherein a conversion formula for each piece of calculation data and the online real-time data is established respectively, and then, a water quality data conversion formula corresponding to each piece of calculation data is determined (See : pg. 9, “3.5 Correlation coefficient method (TOC method)” The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater ). Claim 5. Cai et al discloses t he online model water quality conversion method of claim 1, further comprising: substituting the calculation data into an ASM1 water plant full-process simulation model to simulate effluent quality (See : pg. 9, “3.5 Correlation coefficient method (TOC method)”, The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater. The data showed that the linear regression equation could be established between the TOC and COD, and there was a significant correlation between TOC and COD. A linear regression equation between TOC and COD was established by Sun Liyan et al. [32] based on the correlation analysis of COD and TOC of organic pollutants done by 18 surface water quality automatic monitoring stations. For each of the 18 monitoring stations, regression studies were performed base on at least 20 sets of valid data to obtain the TOC-COD conversion curve ; pg. 9 last paragraph, The device combusts water samples in the combustion furnace under the temperature of 1,200 ℃ , uses the infrared analyzer to measure the amount of CO2 generated by combustion, calculates the TOC and then convert TOC to COD. The response time of the instrument is 1 minute and its measuring scope can reach 100~200,000 mg/L. ; Fig. 1, structure of a COD on-line automatic monitoring instrument ) . Claim 6. Cai et al discloses t he online model water quality conversion method of claim 5, further comprising: operating the water quality data conversion model according to the online real-time data, performing real-time conversion to obtain the calculation data (See: Abstract, the on-line automatic monitoring of water quality is particularly urgent; pg. 9, “3.5. Correlation coefficient method (TOC method), The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater. The data showed that the linear regression equation could be established between the TOC and COD, and there was a significant correlation between TOC and COD. A linear regression equation between TOC and COD was established by Sun Liyan et al. [32] based on the correlation analysis of COD and TOC of organic pollutants done by 18 surface water quality automatic monitoring stations. For each of the 18 monitoring stations, regression studies were performed base on at least 20 sets of valid data to obtain the TOC-COD conversion curve. The slope range was 1.03~4.3, the intercept range was -7.38~23.78, and the correlation coefficient was 0.727~0.998, which showed that the correlation between TOC and COD was good; pg. 11 “5. Conclusions”, with the improvement of embedded system and intelligent algorithms, on-line monitors will become more compact, integrated and intelligent. And the remote HMI and data sharing will make the real-time data display and remote operation control of the monitoring instrument much simpler. Besides, Water quality warning and sewage optimization options will become possible with the support of big data and cloud technology. “Monitoring problems-finding problems-early warning issues-optimizing solutions” can be achieved for the COD on-line monitoring equipment) , and storing the calculation data in an online real-time database (See: pg. 2, “2. COD on-line automatic monitoring instrument” a COD on-line automatic monitoring instrument consists of injection system, reaction system, detection and control system, as is shown in figure 1. In the injection system, the water sample is collected, transported and mixed with the reagent, and then the wastewater is discharged and the reaction chamber is cleaned. The reaction system is responsible for the digestion and reaction of the water sample. While the detection-control system is the system for on-line analysis. It includes the monitoring, data acquisition, data processing, display, storage, printout and transmission) ; the ASM1 water plant full-process simulation model calling the calculation data in the online real-time database to simulate the effluent quality of a water plant and being used for simulating the effluent quality of the water plant on line (See : pg. 9, “3.5 Correlation coefficient method (TOC method)”, The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater. The data showed that the linear regression equation could be established between the TOC and COD, and there was a significant correlation between TOC and COD. A linear regression equation between TOC and COD was established by Sun Liyan et al. [32] based on the correlation analysis of COD and TOC of organic pollutants done by 18 surface water quality automatic monitoring stations. For each of the 18 monitoring stations, regression studies were performed base on at least 20 sets of valid data to obtain the TOC-COD conversion curve ; pg. 9 last paragraph, The device combusts water samples in the combustion furnace under the temperature of 1,200 ℃ , uses the infrared analyzer to measure the amount of CO2 generated by combustion, calculates the TOC and then convert TOC to COD. The response time of the instrument is 1 minute and its measuring scope can reach 100~200,000 mg/L. ; Fig. 1, structure of a COD on-line automatic monitoring instrument ) . Claim 7. Cai et al discloses t he online model water quality conversion method of claim 5, further comprising: in a Python environment, establishing the water quality data conversion model and the ASM1 water plant full-process simulation model at the same time (See: pg. 9, “3.5 Correlation coefficient method (TOC method)” The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater ), directly substituting the calculation data obtained by conversion into the ASM1 water plant full-process simulation model (See: pg. 9, “3.5 Correlation coefficient method (TOC method)”, The conversion equation between the TOC and COD was established by Zhang Dan [31], who conducted accurate measurement of COD and TOC in two wastewater samples, the chlorine organic chemical wastewater and the chlorine-containing organic and inorganic mixed chemical wastewater and on the basis had the data obtained fitted by a linear regression equation to determine the correlation between COD and TOC of high chlorine wastewater. The data showed that the linear regression equation could be established between the TOC and COD, and there was a significant correlation between TOC and COD. A linear regression equation between TOC and COD was established by Sun Liyan et al. [32] based on the correlation analysis of COD and TOC of organic pollutants done by 18 surface water quality automatic monitoring stations. For each of the 18 monitoring stations, regression studies were performed base on at least 20 sets of valid data to obtain the TOC-COD conversion curve ; pg. 9 last paragraph, The device combusts water samples in the combustion furnace under the temperature of 1,200 ℃ , uses the infrared analyzer to measure the amount of CO2 generated by combustion, calculates the TOC and then convert TOC to COD. The response time of the instrument is 1 minute and its measuring scope can reach 100~200,000 mg/L. ), and outputting an effluent quality result of the water plant and storing it in an online server database (See: pg. 2, “2. COD on-line automatic monitoring instrument” a COD on-line automatic monitoring instrument consists of injection system, reaction system, detection and control system, as is shown in figure 1. In the injection system, the water sample is collected, transported and mixed with the reagent, and then the wastewater is discharged and the reaction chamber is cleaned. The reaction system is responsible for the digestion and reaction of the water sample. While the detection-control system is the system for on-line analysis. It includes the monitoring, data acquisition, data processing, display, storage, printout and transmission ). As per Claims 8-10: The instant claims recite substantially same limitation as the above rejected claim 1, and therefore rejected under the same rationale. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT KIBROM K GEBRESILASSIE whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-8571 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 9:00 AM-5:30 PM . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is 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, FILLIN "SPE Name?" \* MERGEFORMAT Rehana Perveen can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571 272 3676 . 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. FILLIN "Examiner Stamp" \* MERGEFORMAT KIBROM K. GEBRESILASSIE Primary Examiner Art Unit 2189 /KIBROM K GEBRESILASSIE/ Primary Examiner, Art Unit 2189 03/06/2026