# US 2024/0201159 A1 · Prediction of water parameters based on multivariate regression trees estimation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0201159 A1 - Document ID: US20240201159A1 - Status: Published application - Kind: A1 - Application number: 18/544,663 - Filed: 2023-12-19 - Published: 2024-06-20 - Earliest claimed: 2022-12-19 - Assignees: Maytronics Ltd. - Applicants: Maytronics Ltd. - Inventors: Bar AMIT; Adva DUVDEVANI; Vishal GUPTA - Web page: https://patentgrep.com/patent/US20240201159A1 ## CPC - G01N 33/1826: {Organic contamination in water} - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G01N 33/18: Water - G01N 33/1853: {Hardness of water} - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning - G06N 3/09: Supervised learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06N 20/20: Ensemble learning - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method of estimating a level of one or more water parameters using trained machine learning (ML) based regression trees, comprising receiving physical features and spectral features relating to one or more sample of waters, propagating the water sample(s) through a regression trees comprising a plurality of nodes arranged in a plurality of branches propagating from a root node to a plurality of leaf nodes, each of the plurality of nodes is associated with an ML model trained to estimate a level of one or more water parameters in water samples, identifying a certain leaf node to which the water sample(s) propagated based on the plurality of physical features, estimating a level of one or more of the water parameters in the water sample(s) by applying the ML model associated with the certain leaf node to the spectral features, and initiating action(s) based on the water parameter(s) estimated level. ## Claims ### Claim 1 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. ### Claim 2 (depends on 1) The method of claim 1, wherein the at least one water parameter is a member of a group consisting of: a chlorine concentration value, a dissolved oxygen concentration value, an ORP value, an alkalinity value, and an acidity value. ### Claim 3 (depends on 1) The method of claim 1, wherein each of the plurality of training samples is associated with a respective value of a level of the at least one water parameter. ### Claim 4 (depends on 1) The method of claim 1, wherein the loss is computed based on the level of the at least one water parameter of the plurality of training samples. ### Claim 5 (depends on 1) The method of claim 1, wherein the plurality of candidate split values are selected to ensure a minimal predefined number of samples is propagated to each of the child nodes. ### Claim 6 (depends on 1) The method of claim 1, wherein in case a minimized loss computed for a certain node does not exceed the loss computed for its parent node, the parent node is not split and is defined as a leaf node. ### Claim 7 (depends on 1) The method of claim 1, wherein each of the ML models associated with one of the at least two child nodes is trained using a first group of training samples selected from one of the subsets which is propagated to the respective child node and tested using a second group of validation samples of the respective subset such that the first group is not overlapping with the second group. ### Claim 8 (depends on 7) The method of claim 7, wherein the first group is larger than the second group. ### Claim 9 (depends on 1) The method of claim 1, wherein the ML models are linear or log based ML models. ### Claim 10 (depends on 1) The method of claim 1, wherein the plurality of physical features are members of group consisting of: temperature, electrical conductivity, acidity, basicity, cyanuric acid, alkalinity, and hardness. ### Claim 11 (depends on 1) The method of claim 1, wherein the plurality of spectral features comprise light absorption values in at least one of a plurality of light spectral regions. ### Claim 12 (depends on 1) The method of claim 1, wherein at least one of the plurality of physical features is expressed in a continuous range. ### Claim 13 (depends on 12) The method of claim 12, wherein the plurality of candidate split values are selected based on a plurality of quantiles segmenting the continuous range of the at least one physical feature. ### Claim 14 (depends on 1) The method of claim 1, wherein at least one of the plurality of physical features is expressed in discrete values. ### Claim 15 (depends on 1) The method of claim 1, wherein the plurality of samples are samples of water of at least one pool. ### Claim 16 (depends on 1) The method of claim 1, further comprising creating a forest of regression trees comprising a plurality of regression trees, each of the plurality of regression trees is created based on a different association of its plurality of nodes with respective ML models. ### Claim 17 A system for creating machine learning (ML) based regression trees trained for estimating a level of at least one water parameter, comprising: at least one processor configured to execute a code, the code comprising: code instructions to use 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 a plurality of the training samples, training two ML models each associated with one of the child nodes, each ML mode is trained using a plurality of spectral features of the propagated samples of a respective one of the two subsets, 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; and code instructions to output the regression tree for estimating the level of the at least one water parameter in at least one new water sample. ### Claim 18 A method of estimating a level of at least one water parameter using trained machine learning (ML) based regression trees, comprising: using at least one processor for: receiving a plurality of physical features and a plurality of spectral features or, in cases with no spectral features, ORP, pH and UV values relating to at least one sample of water, propagating the at least one sample of water through a regression tree comprising a plurality of nodes arranged in a plurality of branches propagating from a root node to a plurality of leaf nodes, each of the plurality of nodes is associated with an ML model trained to estimate a level of at least one water parameter in water samples; identifying a certain leaf node of the plurality of leaf nodes to which the at least one water sample propagated based on the plurality of physical features; estimating a level of the at least one water parameter in the at least one water sample by applying the ML model associated with the certain leaf node to the plurality of spectral features; and initiating at least one action based on the estimated level of the at least one water parameter. ### Claim 19 (depends on 18) The method of claim 18, further comprising estimating the level of the at least one water parameter in the at least one water sample using a forest of regression trees comprising a plurality of regression trees, each of the plurality of regression trees is configured with different association of its plurality of nodes with respective ML models. ### Claim 20 A system for estimating a level of at least one water parameter using trained machine learning (ML) based regression trees, comprising: at least one processor configured to execute a code, the code comprising: code instructions to receive a plurality of physical features and a plurality of spectral features relating to at least one sample of water; code instructions to propagate the at least one sample of water through a regression tree comprising a plurality of nodes arranged in a plurality of branches propagating from a root node to a plurality of leaf nodes, each of the plurality of nodes is associated with an ML model trained to estimate a level of at least one water parameter in water samples; code instructions to identify a certain leaf node of the plurality of leaf nodes to which the at least one water sample propagated based on the plurality of physical features; code instructions to estimate a level of the at least one water parameter in the at least one water sample by applying the ML model associated with the certain leaf node to the plurality of spectral features; and code instructions to initiate at least one action based on the estimated level of the at least one water parameter.