DETAILED ACTION
This office action is in response to application filed on April 30, 2024.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/30/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
[0044]: Language “… an ensemble machine learning unit that receives water usage data labeled as the ‘indoor water leak state’ and multidimensional data on water leak occurrence, and perform seconds machine learning by matching caliber-specific water leak occurrence and the water leak type” should read “… an ensemble machine learning unit that receives water usage data labeled as the ‘indoor water leak state’ and multidimensional data on water leak occurrence, and performs second machine learning by matching caliber-specific water leak occurrence and the water leak type” in order to correct minor informalities.
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language should read:
“An indoor water leak detection and type identification device using multidimensional data, the indoor water leak detection and type identification device comprising:
a metering data collection unit configured to collect water usage data measured from a water meter for remote meter reading;
a controller configured to receive the
a normal use machine learning unit configured to receive data preprocessed on the water usage data in a form of a plurality of input vectors, and to perform first machine learning by performing a convolution operation on a bias;
an ensemble machine learning unit configured to receive the water usage data labeled as the ‘indoor water leak state’ and the multidimensional data on water leak occurrence, and to perform second machine learning by matching caliber-specific water leak occurrence and the water leak type,
wherein the controller is configured to:
classify data on the exceed a first time;
preprocess a missing value, a negative value, and an abnormal value among the
remove the preprocessed missing value, the preprocessed negative value, and the preprocessed abnormal value from a water leak detection target and a type identification target,
wherein the water leak detection target includes a toilet, a pipe rupture, an anti-freezing faucet, a boiler, a water purifier, and a pot waterer,
wherein the ensemble machine learning unit classifies a type of a learning model by using a confusion matrix based on a random forest algorithm,
wherein the type of the learning model is classified into the toilet, the pipe rupture, the anti-freezing faucet, the boiler, the water purifier, and the pot waterer,
wherein the toilet, the pipe rupture, and the anti-freezing faucet are again classified through a slope of a trend line of water usage within a water leak period,
wherein the type of the learning model is classified as the toilet when the slope of the trend line is within ±0.2,
wherein the type of the learning model is classified as the pipe rupture when the slope of the trend line is greater than
wherein the type of the learning model is classified as the anti-freezing faucet when the slope of the trend line is smaller than .
Appropriate correction is required.
Claim 2 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification device of claim 1,
wherein the metering data collection unit collects the water usage data measured from the water meter for the remote meter reading,
wherein the controller receives and preprocesses the water usage data,
wherein the controller calculates the
wherein the controller determines whether the exceed the first time,
wherein the controller classifies the data on the
wherein the normal use machine learning unit receives the .
Appropriate correction is required.
Claim 3 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification device of claim 1,
wherein the controller receives result data, which is obtained by performing the first machine learning, and determines whether there is an indoor water leak,
wherein when it is determined that there is no indoor water leak, the controller labels the result data as ‘normal data’ and the ‘normal state’,
wherein when it is determined that the indoor water leak is present, the controller labels the result data as the ‘indoor water leak state’ and outputs the
wherein the ensemble machine learning unit receives the water usage data labeled as the ‘indoor water leak state’ and the multidimensional data on water leak occurrence, and performs the second machine learning by matching the caliber-specific water leak occurrence and the water leak type” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim 4 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification device of claim 3,
wherein the ensemble machine learning unit simultaneously receives one-dimensional data on the result data labeled as the ‘indoor water leak state’ and the multidimensional data on water leak occurrence, and generates a plurality of indoor water leak learning models by the matching,
wherein the ensemble machine learning unit receives the generated plurality of indoor water leak learning models and evaluates the plurality of indoor water leak learning models, and
wherein the ensemble machine learning unit receives the evaluated plurality of indoor water leak learning models and verifies the evaluated plurality of indoor water leak learning models” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim 5 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification device of claim 1, wherein the multidimensional data includes one or more of a caliber, a cumulative meter reading value, business identification, year of construction, a type and a diameter of a connected pipe, a label, civil complaint data, and facility data” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim 6 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification device of claim 4, wherein the controller receives the verified plurality of indoor water leak learning models and generates the combination data of the ‘normal state’ and the ‘indoor water leak state’, labels the combination data as the ‘normal state’ and ends an operation when the ‘normal state’ is detected, and identifies the water leak type and calculates the water leak amount when the ‘indoor water leak state’ is detected” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim 7 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification device of claim 6,
wherein the controller displays a case that the ‘normal state’ is detected, as ‘T=0 && F=0’
wherein the controller displays a case that the ‘indoor water leak state’ is detected, as ‘T=1 && F=1’,
wherein the controller displays a first false positive case that the ‘indoor water leak state’ is detected in spite of the ‘normal state’, as ‘T=0 && F=1’, and
wherein the controller displays a second false positive case that the ‘normal state’ is detected in spite of the ‘indoor water leak state’, as ‘T=1 && F=0’” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim 8 is objected to because of the following informalities:
Claim language should read:
“An indoor water leak detection and type identification method using multidimensional data, the indoor water leak detection and type identification method comprising:
receiving, by a controller of a device, water usage data measured through remote meter reading, calculating
labeling, by the controller, result data, which is obtained by performing the first machine learning, as ‘indoor water leak state’ in a case of water leak occurrence based on sizes of the the multidimensional data on the water leak occurrence, and performing second machine learning; and
generating, by the controller, combination data of ‘normal state’ and the ‘indoor water leak state’, and performing identification of a water leak type and calculation of a water leak amount when the ‘indoor water leak state’ is detected,
wherein the performing of the first machine learning includes:
calculating, by the controller, the
determining, by the controller, whether the exceed a first time; and
classifying, by the controller, the data as the ‘normal state’ or the ‘indoor water leak state’ based on whether the first time is exceeded,
wherein the controller is configured to:
preprocess a missing value, a negative value, and an abnormal value among the collected water usage data; and
remove the preprocessed missing value, the preprocessed negative value, and the preprocessed abnormal value from a water leak detection target and a type identification target,
wherein the water leak detection target includes a toilet, a pipe rupture, an anti-freezing faucet, a boiler, a water purifier, and a pot waterer,
wherein the ensemble machine learning unit classifies a type of a learning model by using a confusion matrix based on a random forest algorithm,
wherein the type of the learning model is classified into the toilet, the pipe rupture, the anti-freezing faucet, the boiler, the water purifier, and the pot waterer,
wherein the toilet, the pipe rupture, and the anti-freezing faucet are again classified through a slope of a trend line of water usage within a water leak period,
wherein the type of the learning model is classified as the toilet when the slope of the trend line is within ±0.2,
wherein the type of the learning model is classified as the pipe rupture when the slope of the trend line is greater than
wherein the type of the learning model is classified as the anti-freezing faucet when the slope of the trend line is smaller than .
Appropriate correction is required.
Claim 9 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification method of claim 8, wherein the performing of the first machine learning includes:
collecting, by a metering data collection unit, the water usage data measured from a water meter for the remote meter reading;
receiving and preprocessing, by the controller, the water usage data;
calculating, by the controller, the
determining, by the controller, whether the exceed the first time;
classifying, by the controller, the data on the
receiving, by the normal use machine learning unit, the a form of [[the]]a plurality of input vectors, and performing the first machine learning in a time-series manner by performing [[the]]a convolution operation on [[the]]a bias” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim 10 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification method of claim 8, wherein the performing of the second machine learning includes:
receiving, by the controller, the result data, which is obtained by performing the first machine learning, and determining whether there is an indoor water leak;
labeling the result data as ‘normal data’ and the ‘normal state’ when it is determined that there is no indoor water leak, and labeling the result data as the ‘indoor water leak state’ and outputting the
receiving, by the ensemble machine learning unit, the water usage data labeled as the ‘indoor water leak state’ and the multidimensional data on water leak occurrence, and performing the second machine learning by matching [[the]]a caliber-specific water leak occurrence and the water leak type” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim 11 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification method of claim 10, wherein the performing of the second machine learning includes:
simultaneously receiving, by the ensemble machine learning unit, one-dimensional data on the result data labeled as the ‘indoor water leak state’ and the multidimensional data on water leak occurrence, and generating a plurality of indoor water leak learning models by the matching;
receiving, by the ensemble machine learning unit, the generated plurality of indoor water leak learning models and evaluating the plurality of indoor water leak learning models; and
receiving, by the ensemble machine learning unit, the evaluated plurality of indoor water leak learning models and verifying the evaluated plurality of indoor water leak learning models” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim 12 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification method of claim 8, wherein the multidimensional data includes one or more of a caliber, a cumulative meter reading value, business identification, year of construction, a type and a diameter of a connected pipe, a label, civil complaint data, and facility data” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim 13 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification method of claim 11, wherein the performing of the identification of the water leak type and the calculation of the water leak amount includes:
receiving, by the controller, the verified plurality of indoor water leak learning models and generating the combination data of the ‘normal state’ and the ‘indoor water leak state’;
labeling the combination data as the ‘normal state’ and ending an operation when the ‘normal state’ is detected; and
identifying the water leak type and calculating the water leak amount when the ‘indoor water leak state’ is detected” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim 14 is objected to because of the following informalities:
Claim language should read:
“The indoor water leak detection and type identification method of claim 13, wherein the generating of the combination data includes:
displaying, by the controller, a case that the ‘normal state’ is detected, as ‘T=0 && F=0’;
displaying, by the controller, a case that the ‘indoor water leak state’ is detected, as ‘T=1 && F=1’;
displaying, by the controller, a first false positive case that the ‘indoor water leak state’ is detected in spite of the ‘normal state’, as ‘T=0 && F=1’; and
displaying, by the controller, a second false positive case that the ‘normal state’ is detected in spite of the ‘indoor water leak state’, as ‘T=1 && F=0’” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112 (e.g., provide appropriate antecedence basis).
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claims 1-14 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.
Claim 1 recites:
“wherein the type of the learning model is classified as the toilet when the slope of the trend line is within ±0.2,
wherein the type of the learning model is classified as the pipe rupture when the slope of the trend line is greater than or equal to 0.2, and
wherein the type of the learning model is classified as the anti-freezing faucet when the slope of the trend line is smaller than or equal to 0.2”
which is unclear as to what classification is being applied when the slope of the trend line is equal to 0.2: Toilet? Pipe rupture? Or anti-freezing faucet? (i.e., all these classifications apply when the slope is exactly 0.2).
Similar language is recited in independent claim 8, with none of the dependent claims clarifying the recited subject matter.
The original disclosure describes similar language as well (see [0116]-[0120]), and therefore does not provide clarification as to how this recited subject matter should be interpreted.
For examination purposes, claim language is interpreted as indicated in the Claim Objections section.
Examiner’s Note
Claims 1-14 were evaluated for patent eligibility under 35 U.S.C. 101 using the SUBJECT MATTER ELIGIBILITY TEST FOR PRODUCTS AND PROCESSES described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) to determine patent eligibility under 35 U.S.C. 101.
Regarding claim 1, the examiner submits that under Step 1 of the test for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a machine/manufacture, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test (see bold text for judicial exception):
“An indoor water leak detection and type identification device using multidimensional data, the device comprising:
a metering data collection unit configured to collect water usage data measured from a water meter for remote meter reading;
a controller configured to receive the measured water usage data, to calculate first and second measured time intervals of minimum water usage for each customer, to generate combination data of ‘normal state’ and ‘indoor water leak state’, and to perform identification of a water leak type and calculation of a water leak amount when the ‘indoor water leak state’ is detected;
a normal use machine learning unit configured to receive data preprocessed on the water usage data in a form of a plurality of input vectors, and to perform first machine learning by performing a convolution operation on a bias;
an ensemble machine learning unit configured to receive water usage data labeled as the ‘indoor water leak state’ and multidimensional data on water leak occurrence, and to perform second machine learning by matching caliber-specific water leak occurrence and the water leak type,
wherein the controller is configured to:
classify data on the calculated time intervals into the ‘normal state’ or the ‘indoor water leak state’ by determining whether a first time of the data on the calculated time intervals is exceeded;
preprocess a missing value, a negative value, and an abnormal value among the collected water usage data; and
remove the preprocessed missing value, the preprocessed negative value, and the preprocessed abnormal value from a water leak detection target and a type identification target,
wherein the water leak detection target includes a toilet, a pipe rupture, an anti-freezing faucet, a boiler, a water purifier, and a pot waterer,
wherein the ensemble machine learning unit classifies a type of a learning model by using a confusion matrix based on a random forest algorithm,
wherein the type of the learning model is classified into the toilet, the pipe rupture, the anti-freezing faucet, the boiler, the water purifier, and the pot waterer,
wherein the toilet, the pipe rupture, and the anti-freezing faucet are again classified through a slope of a trend line of water usage within a water leak period,
wherein the type of the learning model is classified as the toilet when the slope of the trend line is within ±0.2,
wherein the type of the learning model is classified as the pipe rupture when the slope of the trend line is greater than or equal to 0.2, and
wherein the type of the learning model is classified as the anti-freezing faucet when the slope of the trend line is smaller than or equal to 0.2”
The highlighted limitations, under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using abstract ideas (e.g., mental processes and/or mathematical concepts) to manipulate data (e.g., identify minimum water usage times; calculate the time interval between them; compare the duration of the time interval with a threshold to classify data as “normal state” or “indoor water leak state”; preprocess data; perform machine learning based on mathematical concepts; classify result based on slope analysis; see specification at [0081]-[0082], [0101]-[0107], [0109], [0111]-[0120], [0144]-[0146]). Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, the additional elements recited in the claim (see non-bold text in the analysis above under Step 2A - Prong One of the test):
generally link the use of the judicial exception to a particular technological environment or field of use (e.g., indoor water leak detection and type identification; see MPEP 2106.05(h));
add extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) (see MPEP 2106.05(g));
append the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); and
when considering the claim as a whole, integrate the judicial exception into a practical application by reflecting an improvement to other technology or technical field (e.g., determining type of indoor water leak and water leak amount, see specification at [0014]; [0145]-[0146]) (see MPEP 2106.05(a)).
Therefore, these additional elements, when considered individually and in combination, integrate the judicial exception into a practical application. The claim, when considered as a whole, is eligible at Prong Two of the Revised Step 2A (see 2019 Revised Patent Subject Matter Eligibility Guidance – Revised Step 2A, see also MPEP 2106.04(d)).
Similarly, independent claim 8 is directed to patent eligible subject matter as explained above with regards to claim 1.
Regarding the dependent claims 2-7 and 9-14, they were found to be patent eligible under 35 U.S.C. 101 by incorporating the eligible subject matter of their corresponding independent claims.
Subject Matter Not Rejected Over Prior Art
Claims 1-14 are distinguished over the prior art of record for the following reasons:
Regarding claim 1.
Fuentes (Fuentes, H., Mauricio, D. Smart water consumption measurement system for houses using IoT and cloud computing. Environ Monit Assess 192, 602 (2020). https://doi.org/10.1007/s10661-020-08535-4) discloses/teaches:
An indoor water leak detection and type identification device using multidimensional data (Fig. 1, Abstract: an intelligent water system consumption measurement system is presented), the device comprising:
a metering data collection unit configured to collect water usage data measured from a water meter for remote meter reading (Figs. 1-2, item “1. House Data Collection”; p. 6, section “House data collection”: house water consumption data is obtained through a smart water meter that sends this information to a local server for processing);
a controller (Fig. 1, items “2. Edge Gateway” and “4. Cloud”) configured to receive the measured water usage data, to calculate first and second measured time intervals of minimum water usage for each customer, to generate combination data of ‘normal state’ and ‘indoor water leak state’, and to perform identification of a water leak (p. 4-5, section “Smart water consumption measurement system”; p. 6-7, sections “Edge gateway”, “Cloud” and “Water leak detection algorithm”: water consumption data is collected and send for processing using a water leak algorithm for identifying water consumption scenarios including leaks (see also p. 8-13: regarding different scenarios being identified including normal consumption (normal state) and anomalous consume non-zero (indoor water leak state))).
The closest prior art of record, taken individually or in combination, fail to teach or suggest:
“a controller configured to perform identification of a water leak type and calculation of a water leak amount when the ‘indoor water leak state’ is detected;
a normal use machine learning unit configured to receive data preprocessed on the water usage data in a form of a plurality of input vectors, and to perform first machine learning by performing a convolution operation on a bias;
an ensemble machine learning unit configured to receive water usage data labeled as the ‘indoor water leak state’ and multidimensional data on water leak occurrence, and to perform second machine learning by matching caliber-specific water leak occurrence and the water leak type,
wherein the controller is configured to:
classify data on the calculated time intervals into the ‘normal state’ or the ‘indoor water leak state’ by determining whether a first time of the data on the calculated time intervals is exceeded;
preprocess a missing value, a negative value, and an abnormal value among the collected water usage data; and
remove the preprocessed missing value, the preprocessed negative value, and the preprocessed abnormal value from a water leak detection target and a type identification target,
wherein the water leak detection target includes a toilet, a pipe rupture, an anti-freezing faucet, a boiler, a water purifier, and a pot waterer,
wherein the ensemble machine learning unit classifies a type of a learning model by using a confusion matrix based on a random forest algorithm,
wherein the type of the learning model is classified into the toilet, the pipe rupture, the anti-freezing faucet, the boiler, the water purifier, and the pot waterer,
wherein the toilet, the pipe rupture, and the anti-freezing faucet are again classified through a slope of a trend line of water usage within a water leak period,
wherein the type of the learning model is classified as the toilet when the slope of the trend line is within ±0.2,
wherein the type of the learning model is classified as the pipe rupture when the slope of the trend line is greater than or equal to 0.2, and
wherein the type of the learning model is classified as the anti-freezing faucet when the slope of the trend line is smaller than or equal to 0.2”
in combination with all other limitations within the claim, as claimed and defined by the applicant (the examiner submits that the prior art of record does not identify periods of minimum water consumption for obtaining data for machine learning implementation and slope analysis for identification, classification and quantization of indoor water leaks).
Regarding claim 8.
Fuentes (Fuentes, H., Mauricio, D. Smart water consumption measurement system for houses using IoT and cloud computing. Environ Monit Assess 192, 602 (2020). https://doi.org/10.1007/s10661-020-08535-4) discloses/teaches:
An indoor water leak detection and type identification method using multidimensional data (Fig. 1, Abstract: an intelligent water system consumption measurement system is presented), the method comprising:
receiving, by a controller (Fig. 1, items “2. Edge Gateway” and “4. Cloud”) of a device, water usage data measured through remote meter reading (Figs. 1-2, item “1. House Data Collection”; p. 6, section “House data collection”: house water consumption data is obtained through a smart water meter that sends this information to a local server for processing), calculating first and second measured time intervals of minimum water usage for each consumer; and
generating, by the controller, combination data of ‘normal state’ and the ‘indoor water leak state’, and performing identification of a water leak (p. 4-5, section “Smart water consumption measurement system”; p. 6-7, sections “Edge gateway”, “Cloud” and “Water leak detection algorithm”: water consumption data is collected and send for processing using a water leak algorithm for identifying water consumption scenarios including leaks (see also p. 8-13: regarding different scenarios being identified including normal consumption (normal state) and anomalous consume non-zero (indoor water leak state))).
The closest prior art of record, taken individually or in combination, fail to teach or suggest:
“performing, by a normal use machine learning unit, first machine learning on the water usage data;
labeling, by the controller, result data, which is obtained by performing the first machine learning, as ‘indoor water leak state’ in a case of water leak occurrence based on sizes of the calculated time intervals, receiving, by an ensemble machine learning unit, multidimensional data on the water leak occurrence, and performing second machine learning; and
performing identification of a water leak type and calculation of a water leak amount when the ‘indoor water leak state’ is detected,
wherein the performing of the first machine learning includes:
calculating, by the controller, the first and second measured time intervals of the minimum water usage for each consumer;
determining, by the controller, whether the first time of data on the calculated time intervals is exceeded; and
classifying, by the controller, the data as the ‘normal state’ or the ‘indoor water leak state’ based on whether the first time is exceeded,
wherein the controller is configured to:
preprocess a missing value, a negative value, and an abnormal value among the collected water usage data; and
remove the preprocessed missing value, the preprocessed negative value, and the preprocessed abnormal value from a water leak detection target and a type identification target,
wherein the water leak detection target includes a toilet, a pipe rupture, an anti-freezing faucet, a boiler, a water purifier, and a pot waterer,
wherein the ensemble machine learning unit classifies a type of a learning model by using a confusion matrix based on a random forest algorithm,
wherein the type of the learning model is classified into the toilet, the pipe rupture, the anti-freezing faucet, the boiler, the water purifier, and the pot waterer,
wherein the toilet, the pipe rupture, and the anti-freezing faucet are again classified through a slope of a trend line of water usage within a water leak period,
wherein the type of the learning model is classified as the toilet when the slope of the trend line is within ±0.2, wherein the type of the learning model is classified as the pipe rupture when the slope of the trend line is greater than or equal to 0.2, and
wherein the type of the learning model is classified as the anti-freezing faucet when the slope of the trend line is smaller than or equal to 0.2”
in combination with all other limitations within the claim, as claimed and defined by the applicant (the examiner submits that the prior art of record does not identify periods of minimum water consumption for obtaining data for machine learning implementation and slope analysis for identification, classification and quantization of indoor water leaks).
Regarding claims 2-7 and 9-14.
They are also distinguished over the prior art of record due to their dependency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Badawy; Wael et al., US 20160055653 A1, VIDEO BASED INDOOR LEAK DETECTION
Reference discloses detecting indoor liquid leak in equipment such as a pump by comparing video with reference images.
BAILEY; Samuel et al., US 20200340220 A1, Leak Detection Method and Apparatus
Reference discloses estimating position and height information of leaks in a water system using pressure sensor information.
Banerjee; Salil P. et al., US 20190154539 A1, PASSIVE LEAK DETECTION FOR BUILDING WATER SUPPLY
Reference discloses detecting leaks using temperature information.
Enev; Miroslav et al., US 20170131174 A1¸ WATER LEAK DETECTION USING PRESSURE SENSING
Reference discloses use of pressure sensors to determine water leaks in a structure such as a home.
Farah, E., Shahrour, I. Leakage Detection Using Smart Water System: Combination of Water Balance and Automated Minimum Night Flow. Water Resour Manage 31, 4821–4833 (2017). https://doi.org/10.1007/s11269-017-1780-9
Reference discloses determining a water leak based on the minimum night flow approach.
Klicpera; Michael Edward, US 20190234786 A1, Water Meter and Leak Detection System
Reference discloses a water meter and leak detection system that collects water data and transmits information to remote computer or server for analysis.
PERSI DEL MARMO; Paolo, US 20210181053 A1, METHOD FOR IDENTIFYING A LEAKAGE IN A CONDUIT IN WHICH A FLUID FLOWS AND SYSTEM THEREOF
Reference discloses leakage identification in conduits based on mechanical deformation inside an optical fiber cable.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINA CORDERO whose telephone number is (571)272-9969. The examiner can normally be reached 9:30 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANDREW SCHECHTER can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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