DETAILED ACTION
This action is filed in response to the application filed on 7/18/2023.
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 .
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 1-4 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more.
Claim 1 recites the following abstract concepts in BOLD of:
A method for extracting a black-odorous water body based on a Classification and Regression Tree (CART) classification model, comprising the following steps: step 1): selecting a research region, and designing a plurality of sampling points within the research region;
step 2): monitoring relevant chemical indicators of the water body at various sampling points, respectively, extracting remote sensing reflectance data of the water body, sending the relevant chemical indicators and the data to a laboratory, and determining a type of the water body according to a classification standard of relevant chemical indicators for an urban black- odorous water body, wherein the relevant chemical indicators comprise transparency, dissolved oxygen, oxidation-reduction potential, and ammonia nitrogen;
step 3): comparing and analyzing the remote sensing reflectance data extracted at the various sampling points to obtain spectral change features of the black-odorous water body and a general water body, wherein the spectral change features comprise a reflectance difference value between a green band and a red band, a reflectance value of a near infrared band, and a sum of reflectance values of a visible light band;
step 4): constructing each node of a decision tree according to the spectral change features and based on a Gini index, constructing a decision tree classification model to obtain classification results of the black-odorous water body and the general water body, and calculating a classification accuracy; and
step 5): analyzing the classification results to obtain spatiotemporal distribution changes of black-odorous water bodies in the research region.
Under Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category as claim 1 recites a method.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps. The steps of monitoring relevant chemical indicators, determining a type of the water body, comparing and analyzing the remote sensing reflectance data, and analyzing classification results can all be interpreted as a mental process that can be performed in the human mind. While the step of calculating a classification accuracy can be interpreted as performing mathematics.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that the claimed methods and system are not tied to a particular machine or apparatus. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitation teaching selecting a research region and designating sampling points recites necessary data gathering and does not integrate the abstract idea into a practical application. The limitation amounts to necessary data gathering and outputting. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Furthermore, the limitations teaching sending relevant chemical indicators to a laboratory and constructing nodes of a decision tree along with the decision tree itself recites outputting data which does not integrate the abstract idea into a practical application. As recited in MPEP section 2106.05(g), displaying analysis/results is considered extra solution activity. See MPEP 2106.05(g) “Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55”, see also MPEP 2106.05(h), As a whole the claim itself is analogous to the Electric Power Group decision in which it was determined that “ Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).”
Claims 2-4 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea:
Claim 2 further defines the data gathered and does not integrate the abstract idea into a practical application. The limitation amounts to necessary data gathering and outputting. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Claim 3 further limits the mental process of determining a type of water.
Claim 4 further limits the mathematics performed.
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.
Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Dong (CN113887493 A) in view of Li (CN109740645 A).
Regarding Claim 1, Dong teaches a method for extracting a black-odorous water body (e.g. see [0001] “The present invention relates to the technical field of high-resolution remote sensing image information extraction, and in particular to a black and odorous water body remote sensing image recognition method based on an ID3 algorithm”) based on a classification model (e.g. see [0010] “Step 5: Based on the classification results, the decision tree constructed in ENVI is used to classify the black and odorous water bodies in the remote sensing image”) comprising the following steps: step 1): selecting a research region, and designing a plurality of sampling points within the research region (e.g. see [0073] “First, the experimental research area and research objects were determined. The pond water bodies in the research area were taken as the research objects. Through field surveys, several pond points were set up and water quality parameter data at each point were collected”);
step 2): monitoring relevant chemical indicators of the water body at various sampling points, respectively (e.g. see [0007] “Extract water body information, collect and measure relevant chemical indicators on the spot, and classify the water body according to chemical judgment standards”), extracting remote sensing reflectance data of the water body (e.g. see [0008] “Step 3: Extract the remote sensing reflectance of the red, green, blue and near-red bands of the high-resolution remote sensing image, perform band reorganization and select spectral features”), sending the relevant chemical indicators and the data to a laboratory (e.g. see [0020] “Determination of redox potential and ammonia nitrogen content in the laboratory”); and determining a type of the water body according to a classification standard of relevant chemical indicators for an urban black- odorous water body (e.g. see [0053] “Extract water body information, collect and measure relevant chemical indicators on site, and classify water bodies according to chemical judgment standards”) wherein the relevant chemical indicators comprise transparency, dissolved oxygen, oxidation-reduction potential, and ammonia nitrogen (e.g. see [0073] “Through field surveys, several pond points were set up and water quality parameter data at each point were collected, including redox potential, dissolved oxygen, transparency, and ammonia nitrogen”) ;
step 3): comparing and analyzing the remote sensing reflectance data extracted at the various sampling points to obtain spectral change features of the black-odorous water body and a general water body, wherein the spectral change features comprise a reflectance difference value between a green band and a red band, a reflectance value of a near infrared band, and a sum of reflectance values of a visible light band (e.g. see [0078]); and
step 4): constructing each node of a decision tree (e.g. see [0027] “the classification feature of each node is calculated according to information gain, and then entropy and energy are used to construct a classification decision tree”) constructing a decision tree classification model to obtain classification results of the black-odorous water body and the general water body, and calculating a classification accuracy (e.g. see [0010]).
step 5): analyzing the classification results to obtain spatiotemporal distribution changes of black-odorous water bodies in the research region (e.g. see [0079]) .
While Dong teaches utilizing a classification model and constructing a decision tree, Dong does not explicitly disclose a method for extracting a black-odorous water body based on a Classification and Regression Tree classification model; and step 4): constructing each node of a decision tree according to the spectral change features and based on a Gini index.
In the same field of endeavor, Li teaches a method for extracting a black-odorous water body based on a Classification and Regression Tree classification model (e.g. see [0006] “Technical problem solved by the present invention: In view of the defects of the existing technology, the present invention provides a CART decision tree classification method suitable for GF-1 images, which can further improve the classification accuracy of GF-1 images”); and step 4): constructing each node of a decision tree according to the spectral change features and based on a Gini index (e.g. see [0008]).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the classification embodiment of Dong with the CART classification model of Li for the purpose of classifying water types with the advantage of a model that automatically identifies the most important variables to consider in order to obtain the most accurate classification.
Regarding Claim 2, Dong and Li teach the limitations of Claim 1. Dong further discloses wherein the plurality of sampling points in step 1) are designed according to a principle of random distribution (e.g. see [0062-0063] “Step 4: Use random sampling to create training sets and validation sets, train the ID3 algorithm based on the selected feature training set, calculate the classification features of each node based on information gain, and then use entropy and energy to construct a classification decision tree, specifically including 1) Two-thirds of black and odorous water bodies and two-thirds of normal water bodies were randomly selected as training sets, and the remaining samples were used as validation sets”).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Dong (CN113887493 A) in view of Li (CN109740645 A) and in further view of ( Q. Shen et al., "A CIE Color Purity Algorithm to Detect Black and Odorous Water in Urban Rivers Using High-Resolution Multispectral Remote Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6577-6590, Sept. 2019, doi: 10.1109/TGRS.2019.2907283; hereinafter “Shen”).
Regarding Claim 3, Dong and Li teach the limitations of Claim 1. While Dong teaches classifying the water type based on chemical indicator requirements, Dong does not explicitly disclose wherein the classification standard of urban black-odorous water body in step 2) at least satisfies one of relevant chemical indicator requirements as follows: transparency in a range of 0 cm to 25 cm, dissolved oxygen in a range of 0 mg/L to 2mg/L, oxidation-reduction potential in a range of 0 mV to 50 mV, and ammonia nitrogen of not less than 8 mg/L.
In the same field of endeavor, Shen teaches wherein the classification standard of urban black-odorous water body in step 2) at least satisfies one of relevant chemical indicator requirements as follows: transparency in a range of 0 cm to 25 cm, dissolved oxygen in a range of 0 mg/L to 2mg/L, oxidation-reduction potential in a range of 0 mV to 50 mV, and ammonia nitrogen of not less than 8 mg/L (e.g. see [pg. 6578, Table 1]).
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It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the chemical indicators of Dong with the specific ranges of Shen for the purpose of classifying the water type with the advantage of a universal scale to ensure consistent classifications.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Dong (CN113887493 A) in view of Li (CN109740645 A) and in further view of Sokolov (US20220003798 A1).
Regarding Claim 4, Dong and Li teach the limitations of Claim 1. Dong does not explicitly disclose wherein a calculation formula of the Gini index in step 4) is:
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wherein K is a number of contained categories, and pk is a probability of a k-th category.
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In the same field of endeavor, Sokolov teaches wherein a calculation formula of the Gini index in step 4) is:
wherein K is a number of contained categories and pk is a probability of a k-th category (e.g. see [0145-0147] and [Equation [00010] and [Equation [00011]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method for extracting black odorous water bodies of Dong as modified by Li with the Gini index formula of Sokolov for the purpose of classifying the water body with the advantage of utilizing a CART model that automatically identifies the most important variables to consider in order to obtain the most accurate classification.
Conclusion
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/NYLA GAVIA/Examiner, Art Unit 2863
/LISA M CAPUTO/Supervisory Patent Examiner, Art Unit 2863