DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-9 are presented for examination.
Claim Rejections - 35 USC § 101
3. 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.
3.1 Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Is the claim directed to a statutory category?
Yes. The claims are to a method (claim 1), comprising a plurality of method steps.
Step 2A- Prong One
The claim(s) recite(s) a carbon measurement method (claim 1), for unorganized emissions of greenhouse gas in an industrial park, comprising: The step of: “S1 dividing the historical meteorological data into three categories, and describing the historical meteorological data by cubic spline interpolation”, S3 “determining whether a prediction accuracy meets needs of actual industrial parks and related industry standards and laws and regulations for unorganized emission monitoring”, “S4. according to different classifications, carrying out a weight analysis of an important degree quantitative index of an input parameters of Ansys Fluent”, “S5. establishing an Ansys Fluent simulation model of industrial park”, “S6. obtaining an unorganized emission flow of greenhouse gases at a predetermined time node in the industrial park, … “obtaining carbon emissions of the greenhouse gas in the industrial parks”, under the broadest reasonable interpretation fall under a mathematical concept or otherwise a mental process. Therefore, the claims are directed to an abstract idea, by use of generic computer components and thus are clearly directed to an abstract idea, as constructed.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional limitation such as: “a back-propagation (BP) neural network”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities which can be of any type, including general-purpose computer previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “S1 obtaining historical meteorological data and historical carbon emission data of the industrial park, under a support of meteorological theory”; “S2. based on classification, obtaining meteorological data, wherein the meteorological data are difficult to measure and have great influence by an interpolation surface, and inputting the obtained meteorological data into … for strengthening to obtain enhanced meteorological data”, “S3. inputting the enhanced meteorological data into a random forest algorithm for an accurate prediction”, “importing parameters after a weight analysis into the Ansys Fluent simulation model”; “obtaining an unorganized emission concentration of greenhouse gases from a sensor”, under the broadest reasonable interpretation, reasonable fall under data gathering and processing activities that are pre-solution activities, and the step of: “if the requirements are met, outputting prediction results and prediction models, wherein the prediction model is with a strongest generalization ability”; “S7. outputting a random forest prediction model with a strongest generalization ability and an environmental impact factor evaluation report” could amount to insignificant post-solution activities that are also well-known, routine and conventional activities and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as previously discussed above with reference to the integration of abstract idea into a practical application, the additional elements of: “a back-propagation (BP) neural network”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities which can be of any type, including general-purpose computer previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “S1 obtaining historical meteorological data and historical carbon emission data of the industrial park, under a support of meteorological theory”; “S2. based on classification, obtaining meteorological data, wherein the meteorological data are difficult to measure and have great influence by an interpolation surface, and inputting the obtained meteorological data into … for strengthening to obtain enhanced meteorological data”, “S3. inputting the enhanced meteorological data into a random forest algorithm for an accurate prediction”, “importing parameters after a weight analysis into the Ansys Fluent simulation model”; “obtaining an unorganized emission concentration of greenhouse gases from a sensor”, under the broadest reasonable interpretation, reasonable fall under data gathering and processing activities that are pre-solution activities, and the step of: “if the requirements are met, outputting prediction results and prediction models, wherein the prediction model is with a strongest generalization ability”; “S7. outputting a random forest prediction model with a strongest generalization ability and an environmental impact factor evaluation report” could amount to insignificant post-solution activities that are also well-known, routine and conventional activities and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101. Therefore, using computer components amount to no more than mere instructions to perform the abstract, and thus are not sufficient to amount to significantly more than the recited abstract, as constructed.
3.2 Dependent claims 2-9 merely include limitations pertaining to further mathematical computations (claim 2), “wherein three categories of meteorological data in step S1 comprise: the first category: a sensible heat flux, a potential temperature gradient, a surface ripple ratio, a surface reflectivity at noon, an air temperature, a temperature measurement height, a humidity, a radiation loss rate, and a total heat release rate: the second category: a surface friction velocity, a convective velocity scale, a convective boundary layer height, a mechanical atmospheric boundary layer height, a surface roughness, a wind speed, a wind direction, a wind measurement height, and a humidity; the third category: a Mani length, a surface pressure, a low-capacity, a humidity, a temperature, an industrial park latitude and longitude, and an elevation” (mental process or otherwise a mathematical concept). (claim 3); “wherein for the first category of meteorological data, the sensible heat flux is data having a greatest impact on the unorganized emissions, through a thought of interpolation, describing a sensible heat flux of temperature at a corresponding time node, by an interpolation surface, obtaining a sensible heat flux corresponding to a predetermined temperature value on the corresponding time node” (mental process or otherwise a mathematical concept); (claim 4); “wherein for the second category of meteorological data, data with a greatest impact on the unorganized emissions are the surface friction velocity and the mechanical atmospheric boundary layer height, through the thought of interpolation, obtaining the surface friction velocity and the mechanical atmospheric boundary layer height of the wind speed at the corresponding time node, by an interpolation surface, obtaining the surface friction velocity and mechanical atmospheric boundary layer height under a predetermined wind speed condition at the corresponding time node” (mathematical concept or otherwise mental process); (claim 5); “wherein for the third category of meteorological data, the Mani length is data having a greatest impact on the unorganized emissions, using the thought of interpolation fitting, fitting out the Mani length of humidity at the corresponding time node, by the interpolation surface, obtaining the Mani length corresponding to a humidity value on the corresponding time node” (a mental process or otherwise a mathematical concept); (claim 6); “wherein the data enhancement of the BP neural network in step S2 is specified as: putting the parameter values obtained by the interpolation surface into the whole category of meteorological factors respectively for learning reinforcement, repairing and strengthening missing data and error data that affect a key meteorology based on relevant meteorological theories, making a reduction degree of the missing data and error data reach more than 90%” (a mental process or otherwise a mathematical concept); (claim 7) “wherein the weight analysis in step S4 is specified as: based on the prediction model in step S3 and the cubic spline interpolation function, combined with an average impurity reduction of random forest, quantifying an importance of important feature value, as well as the surface friction velocity, Mani length, sensible heat flux, and mechanical atmospheric boundary layer height obtained by the cubic spline interpolation, and the wind speed, wind direction, temperature and humidity measured by the sensor at a low cost, quantifying the surface friction velocity, the Mani length, the sensible heat flux, the mechanical atmospheric boundary layer height, the wind speed, the wind direction, the temperature, and the humidity into quantitative indicators based on the importance, and performing the weight analysis” (a mathematical concept or otherwise a mental process); (claim 8) “wherein in Step S5, by using a Navier-Stokes continuity equation, a Navier-Stokes momentum equation, an energy equation, relying on an Ansys Fluent simulation software, combined with the parameters obtained by the weight analysis, as an input of the model established by the Ansys Fluent simulation software, obtaining a fluid flow model of the unorganized emission in the industrial park” (a mathematical concept); (claim 9) “wherein in Step S6, multiplying a specific flow value of the unorganized emissions at a predetermined moment with a concentration value of greenhouse gas unorganized emissions measured by the sensors, and obtaining a carbon measurement for the unorganized emissions of greenhouse gas in the industrial parks” (a mathematical concept); all of which further amount to further mathematical concept and/or mental process similar to that already recited by the independent claims and already addressed above and thus are further not patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 112
4. 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.
4.1 Claims 1-9 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. The claims in step S2 provide for “obtaining meteorological data, wherein the meteorological data are difficult to measure”, it is unclear how and/or the level of which the data is difficult to measure and what makes it possible to measure, as claimed, which lead to lack of clarity. Furthermore, the claims recite “and have great influence by an interpolation surface” without stating how influence that is or if anything how great said influence is and what makes such great of an influence. In step S3, “wherein the prediction model is with a strongest generalization ability” further leads to lack of clarity as to how strong a generalization ability associated with said prediction model or what is meant by the term. Further clarification is respectfully requested in response to this office correspondence. Step S4 further “an important degree quantitative index of an input parameters” is further unclear as to what is meant by an important degree… and what makes it so important. Further clarification is respectfully requested in response to this office correspondence. Further in step S5 the recitation of “establishing an Ansys Fluent simulation model of industrial park”, it is unclear in the claim what is used as input to establish said Ansys Fluent simulation model and how said Ansys Fluent simulation model as claimed. It is also not clear what happened after importing parameters after a weight analysis into the Ansys Fluent simulation model, as the claim does not set forth what the imported parameters are used for. Further clarification is respectfully requested in response to this office correspondence. In the like manner, step S6 recites “obtaining an unorganized emission flow of greenhouse gases at a predetermined time node in the industrial park”, “obtaining carbon emissions of the greenhouse gas in the industrial parks” without stating how said obtaining could be achieved. Further clarification is respectfully requested in response to this office correspondence. Similarly, random forest prediction model with a strongest generalization ability in step S7 also lack clear and thus is indefinite in the claim.
4.2 Claim 2-5 recite the limitation "the first category”, “the second category”, “the third category”, respectively in lines 4, 7, and 11. Also see claim 3 line 2 (“the first category”); claim 4 line 2 (“the second category”); claim 5 line 2 (“the third category”). There is insufficient antecedent basis for this limitation in the claim.
4.3 Claim 6 recites the limitation "the whole category” in lines 4-5. There is insufficient antecedent basis for this limitation in the claim.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
5.1 Fu et al. (USPG_PUB No. 2011/0304619) teaches a method that provides the ability to extract a primitive quadric surface from unorganized point cloud data.
5.2 Yellin et al. (USPG_PUB No. 2012/0323499) teaches a system and method for determining amounts of carbon dioxide, other greenhouse gas (GHG), and/or toxic gaseous emissions from a mobile or stationary emissions source such as automobiles having an internal combustion engine, fossil-fuel or other hydrocarbon-burning facilities, or the like.
5.3 Eichenlaub et al. (USPG_PUB No. 2023/0304982) teaches a system and method for detecting emission from a target emission source.
6. Claims 1-9 are rejected and this action is non-final. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-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, 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, EMERSON C PUENTE can be reached at 571-272-3652. 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.
/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 September 30, 2026
Prosecution Insights