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 .
DETAILED ACTION
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09 June 2024 and 16 June 2024 were filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The abstract of the disclosure is objected to because the word “comprising” should be changed to --including--. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 (and dependent claims 2-16) recite “A method of creating machine learning (ML) based regression trees trained for estimating a level of at least one water parameter, comprising: using at least one processor for: using a plurality of training samples relating to a plurality of water samples to create a regression tree comprising a plurality of nodes arranged in a plurality of branches extending from a root node to a plurality of leaf nodes by splitting each of the plurality of nodes into at least two respective child nodes by: splitting a set of the plurality of training samples which propagated to the respective node into at least two subsets based on each of a plurality of candidate split values applied for each of at least one of a plurality of physical features of the propagated samples, for each of the candidate splits, training two ML models each associated with one of the child nodes, each ML model is trained using a plurality of spectral features of the propagated samples of a respective one of the two subsets and its loss is computed, and selecting an optimal split value from the plurality of candidate split values by minimizing loss of each of the trained ML models applied to estimate a level of at least one water parameter in the propagated samples of the respective subset or not splitting if neither split improves the loss; and outputting the regression tree for estimating the level of the at least one water parameter in at least one new water sample.”
Claims 1-16, in view of the claim limitations, recite the abstract idea of “using at least one processor for: using a plurality of training samples relating to a plurality of water samples to create a regression tree comprising a plurality of nodes arranged in a plurality of branches extending from a root node to a plurality of leaf nodes by splitting each of the plurality of nodes into at least two respective child nodes by: splitting a set of the plurality of training samples which propagated to the respective node into at least two subsets based on each of a plurality of candidate split values applied for each of at least one of a plurality of physical features of the propagated samples, for each of the candidate splits, training two ML models each associated with one of the child nodes, each ML model is trained using a plurality of spectral features of the propagated samples of a respective one of the two subsets and its loss is computed, and selecting an optimal split value from the plurality of candidate split values by minimizing loss of each of the trained ML models applied to estimate a level of at least one water parameter in the propagated samples of the respective subset or not splitting if neither split improves the loss; and outputting the regression tree for estimating the level of the at least one water parameter in at least one new water sample.”
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited “using at least one processor for: using a plurality of training samples relating to a plurality of water samples to create a regression tree comprising a plurality of nodes arranged in a plurality of branches extending from a root node to a plurality of leaf nodes by splitting each of the plurality of nodes into at least two respective child nodes by: splitting a set of the plurality of training samples which propagated to the respective node into at least two subsets based on each of a plurality of candidate split values applied for each of at least one of a plurality of physical features of the propagated samples, for each of the candidate splits, training two ML models each associated with one of the child nodes, each ML model is trained using a plurality of spectral features of the propagated samples of a respective one of the two subsets and its loss is computed, and selecting an optimal split value from the plurality of candidate split values by minimizing loss of each of the trained ML models applied to estimate a level of at least one water parameter in the propagated samples of the respective subset or not splitting if neither split improves the loss; and outputting the regression tree for estimating the level of the at least one water parameter in at least one new water sample.”; therefore, the claims recite mental processes and mathematical concepts. Accordingly, the claims recite a mental process and mathematical concept, and thus, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of“[a] computer- implemented method” and “the method is carried out by one or more physical processors configured by machine-readable instructions” as recited in claims 1, 17, 18 and 20, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-16 and 19 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0196]-[0198] (describing that the disclosure is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-16 and 19 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea. Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Holzl et al (US 11,961,012) disclose the method processes digital input data having input and output variables and being semantically annotated based on a digital semantic representation having a hierarchical tree structure where each tree in the structure represents an input variable of the data, the leaf nodes of the respective tree being the discrete values of the input variable. Hicks et al (US 11,346,830) disclose a method that includes monitoring, by one or more processors, at least one water sensor to establish a baseline of a water condition model and monitoring one or more water conditions. A predicted water condition is determined based on the water condition model and the one or more water conditions. An alert is transmitted to one or more devices based on determining that the predicted water condition indicates a predicted contaminant level above a threshold. Reese (US 11,093,864) discloses a computing system that computes a variable relevance using a trained tree model. A next child node is selected. A number of observations associated with the next child node is computed. A population ratio value is computed. A next leaf node is selected. First observations are identified. A first impurity value is computed for the first observations. Second observations are identified when the first observations are associated with the descending child nodes. A second impurity value is computed for the second observations. A gain contribution is computed. A node gain value is updated. A variable gain value is updated for a variable associated with the split test. A set of relevant variables is selected based on the variable gain value.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AN H DO whose telephone number is (571)272-2143. The examiner can normally be reached on M-F 7:00am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ricardo Magallanes can be reached on 571-272-5960. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AN H DO/Primary Examiner, Art Unit 2853
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