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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 27, 2026 has been entered.
Status of the Claims
Claims 1-20 are all the claims pending in the application.
Claims 1, 6, 11, 15, and 20 are amended.
Claims 1-20 are rejected.
The following is a Non-Final Office Action in response to amendments and remarks filed March 27, 2026.
Response to Arguments
Regarding the 101 rejections, the rejections are maintained for the following reasons. First, Applicant asserts the rejections should be withdrawn because the claims reflect a technical solution to a technical problem by dynamically monitoring and adjusting estimates of water use, citing ¶[0005] of the present Specification. Examiner respectfully does not find this assertion persuasive because monitoring water usage is not a technical problem but instead reflects the automation of a manual process, see MPEP 2106.05(a) (discussing Credit Acceptance Corp. v. Westlake Services).
Second, Applicant asserts the rejections should be withdrawn because the use of motion sensors recites a particular solution. Examiner respectfully does not find this assertion persuasive because Examiner finds the use of motion sensors is claimed too broadly and generally to be more than a general link to a technological environment. Accordingly the 101 rejections are maintained, please see below for the complete rejections of the claims as amended.
Regarding the 103 rejections, the rejections are maintained for the following reasons. Applicant asserts the rejections should be withdrawn because the cited references only teach reducing use when a home is unoccupied. Examiner respectfully does not find this assertion persuasive because Ribbich contemplates estimating usage to determine faults based on historical data, e.g., ¶¶[ 0278]-[0279]; see also e.g., ¶[0273] discussing occupancy as part of historical data. Accordingly the 103 rejections are maintained, please see below for the complete rejections of the claims as amended.
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.
Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention. Applying Step 1 to the claims it is determined that: claims 1-10 are directed to a process; and claims 11-20 are directed to a machine. Therefore, we proceed to Step 2.
Independent Claims
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
The independent claims recite an abstract idea in the limitations (emphasized):
…obtain a property data set of the property, wherein the property data set includes attributes of the property influencing the water usage of the property;
obtain occupancy data of the property including motion sensor data;
determine occupancy of the property based upon the motion sensor data;
provide the property data set to an ML model trained to generate an estimated water usage of the property associated with the property data set, wherein:
the ML model is trained using a historical training data set representing a plurality of properties selected from a set of properties based upon one or more of the following training data set criteria: property size, household size, types and quantities of water- using appliances, climate zone classification, or water features, wherein the attributes of the property match the one or more training data set criteria;
the historical training data set includes, for the plurality of properties:
a historical water usage data set indicating historical water usage of the plurality of properties; and
a historical property data set including attributes of the plurality of properties influencing the water usage of the plurality of properties; and
the trained ML model is configured to learn relationships between the historical water usage data set and the historical property data set;
adjust the estimated water usage of the property based upon the occupancy of the property;
obtain a water usage data set of the property indicating water usage of the property;
compare the water usage and the adjusted estimated water usage of the property;
responsive to comparing the water usage and the adjusted estimated water usage of the property, generate a notification based upon the water usage and the adjusted estimated water usage comparison exceeding a threshold configured based upon the occupancy data, wherein the notification includes one or more recommendations specific to the attributes of the property; and
provide the notification to a user device.
These limitations recite an abstract idea because these limitations encompass a mental process (i.e., observation, evaluation, judgment, and opinion). That is, these limitations encompass observation (i.e., obtaining property and occupancy data, historical water usage and historical property data), evaluation (i.e., learning relationships between the occupancy, historical water usage and the historical property data), judgment and opinion (i.e., generating an estimated water usage of the property based on occupancy and comparing the water usage and to the estimated water usage to generate the notifications and recommendations). Claims that encompass observation, evaluation, judgment, and opinion fall within the “Mental Processes” grouping of abstract ideas. Claims 1, 11 and 20 recite an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of the independent claims do not integrate the abstract idea into a practical application. The independent claims recite the additional elements (emphasized):
…obtain a property data set of the property, wherein the property data set includes attributes of the property influencing the water usage of the property;
obtaining occupancy data of the property including motion sensor data;
determine occupancy of the property based upon the motion sensor data;
provide the property data set to an ML model trained to generate an estimated water usage of the property associated with the property data set, wherein:
the ML model is trained using a historical training data set representing a plurality of properties selected from a set of properties based upon one or more of the following training data set criteria: property size, household size, types and quantities of water- using appliances, climate zone classification, or water features, wherein the attributes of the property match the one or more training data set criteria;
the historical training data set includes, for the plurality of properties:
a historical water usage data set indicating historical water usage of the plurality of properties; and
a historical property data set including attributes of the plurality of properties influencing the water usage of the plurality of properties; and
the trained ML model is configured to learn relationships between the historical water usage data set and the historical property data set;
adjust the estimated water usage of the property based upon the occupancy of the property;
obtain a water usage data set of the property indicating water usage of the property;
compare the water usage and the adjusted estimated water usage of the property;
responsive to comparing the water usage and the adjusted estimated water usage of the property, generate a notification based upon the water usage and the adjusted estimated water usage comparison exceeding a threshold configured based upon the occupancy data, wherein the notification includes one or more recommendations specific to the attributes of the property; and
provide the notification to a user device.
The additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of using motion sensor data, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (sensor systems) and does not reflect a practical application.
Second, the additional elements of providing the claimed data to a machine learning model to generate the estimate and training the machine learning model, when considered individually or in combination, do not integrate the abstract idea into a practical application because the training and use of machine learning is claimed sufficiently broadly and generally such that it amounts to no more than mere instructions to apply the exception.
Third, the additional elements of providing the notification to a user device, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of sending data, see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Fourth, claims 1, 11, and 20 recite the additional elements of: using a processor to perform the various steps; one or more processors and one or more non-transitory memories storing processor-executable instructions; and a non-transitory computer-readable medium storing processor-executable instructions, respectively. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Claims 1, 11, and 20 are directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to a general link toa field of use and no more than mere instructions to apply the exception. A general link toa field of use and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claims 1, 11 and 20 are not patent eligible.
Dependent Claims
The dependent claims are rejected under 35 USC 101 as directed to an abstract idea for the following reasons.
Claims 2, 3, 12 and 13 recite the same abstract idea because gathering data and using the claimed attributes is still a part of a mental process (i.e., a part of observation data about water use).
Claims 4, 5, and 14 recite the additional elements of updating and retraining the machine learning model and using the specific machine learning techniques. These additional elements are still claimed sufficiently broadly and generally such that it amounts to no more than mere instructions to apply the exception.
Claims 6, 7, 15 and 16 recite the same abstract idea because providing recommendations and an explanation of the water usage is still a part of a mental process (i.e., a part of elevations and judgments data about water use).
Claim 8, 9, 17, and 18 recite essentially the additional elements of gathering water usage data via internet of things sensors and a water meter. These additional elements do not integrate the abstract idea int a practical application because the additional elements are only mere data-gathering, see MPEP 2106.05(g).
Claims 10 and 19 essentially recite the additional elements of generating and providing a dashboard. These additional elements do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of displaying data, see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-4, 6-9, 11-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shayne et al, US Pub. No. 2022/0254004, herein referred to as “Shayne” in view of Ryan et al WO 2024/146700, herein referred to as “Clarke”, further in view of Ribbich et al, US Pub. No. 2017/0234562, herein referred to as “Ribbich”
Regarding claim 1, Shayne teaches:
obtaining, by one or more processors, a property data set of the property wherein the property data set includes attributes of the property influencing the water usage of the property (obtains property information including information the pipes and drains in and connected to the property, ¶[0036] and Fig. 3; see also e.g., ¶[0079] discussing processors);
providing, by the one or more processors, the property data set to an ML model trained (uses trained neural networks, e.g., ¶¶[0040], [0067])
to generate an estimated water usage of the property associated with the property data set, wherein (generates report on a flow of water in an area, or a damage type (e.g., clogged gutter, broken down sprout, broken water hydrant) in the area, ¶¶[0068], [0069]; see also e.g., ¶[0075] discussing detecting a broken pipe):
the ML model is trained using a historical training data set (trained using training data indicative of images or user input indicating water management problems or water damage, ¶¶[0040]-[0041]);
the historical training data set includes: a historical water usage data set indicating historical water usage (trained using training data indicative of images or user input indicating water management problems or water damage, ¶¶[0040]-[0041]);
comparing, by the one or more processors, the water usage and the adjusted estimated water usage of the property (determines a drain likely has an underground clog or other problem based on if the standing water around the drain is estimated to be a larger volume, draining slower than expected, or accumulating faster than expected, ¶[0069]);
responsive to comparing the water usage and the adjusted estimated water usage of the property, generating, by the one or more processors, a notification based upon the water usage and the adjusted estimated water usage comparison exceeding a threshold (generates alert indicative of a problem detected or an emergency detected on the problem, ¶[0070]; see also ¶[0067] discussing thresholds);
wherein the notification includes one or more recommendations specific to the attributes of the property (generates recommendations, e.g., to fix the broken pipe, ¶¶[0071]-[0073]);
and providing, by the one or more processors, the notification to a user device (provides alter to user device, ¶[0070]).
However Shayne does not teach but Clarke does teach:
obtaining, by the one or more processors, a water usage data set of the property indicating water usage of the property (determines waterflow using meter, pg. 10, ll. 14-19);
the ML model is trained using a historical training data set representing a plurality of properties selected from a set of properties based upon one or more of the following training data set criteria: property size, household size, types and quantities of water- using appliances, climate zone classification, or water features, wherein the attributes of the property match the one or more training data set criteria (machine learning models are trained for each type of water consumer (i.e., toilets, faucets, washing machines, etc.) which is trained from data gathered from similar homes/environments, pg. 6, ll. 15-35);
the historical training data set includes, for the plurality of properties: a historical water usage data set indicating historical water usage of the plurality of properties (obtains training data of aggregate water flow data, pg. 6, l.15 – pg. 7, l. 14)
and a historical property data set including attributes of the plurality of properties influencing the water usage of the plurality of properties (obtains training data for various water consumers, e.g., toilets, faucets, washing machines, etc., pg. 6, ll. 15-34);
and the trained ML model is configured to learn relationships between the historical water usage data set and the historical property data set (determines waterflow for each water consumer, pg. 7, l. 15 – pg. 8, l. 9).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the monitoring water of Shayne with the training process of Clarke because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized users of the system in Shayne would likely also be interested in the water use of various features within the home like the toilets, faucets, washing machines, etc. in Clarke and accordingly would have modified Shayne to analyze these features.
However the combination of Shayne and Clarke does not teach but Ribbich does teach:
obtaining, by one or more processors, occupancy data of the property including motion sensor data (occupancy sensors including a motion detector, ¶[0117]);
determining, by the one or more processors, occupancy of the property based upon the motion sensor data (determines occupancy using motion, e.g., ¶¶[0171], [0175], [0274]);
adjusting, by the one or more processors, the estimated water usage of the property based upon the occupancy of the property (estimates usage to determine faults based on historical data, ¶¶[00278]-[0279]; see also ¶[0273] discussing occupancy as a part of historical data);
generating, by the one or more processors, a notification based upon the water usage and the adjusted estimated water usage comparison exceeding a threshold configured based upon the occupancy data (determines faults in home equipment based on thresholds, ¶[0196] and Fig. 9A; see also e.g., ¶[0367] noting system uses a neural network),
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the monitoring water of Shayne and Clarke with the occupancy monitoring of Ribbich because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the analysis of household water usage would likely be improved by monitoring the people in the house, e.g., as taught by Ribbich.
Regarding claim 2, the combination of Shayne, Clarke and Ribbich teaches all the limitations of claim 1 and Clarke further teaches:
wherein the historical water usage data set is generated by obtaining a plurality of historical water usage data entries corresponding to the plurality of properties from a plurality of data providers respectively associated with a corresponding property of the plurality of properties (obtains training data from similar homes, pg. 6, ll. 15 – 34).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the monitoring water of Shayne with the training process of Clarke because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized users of the system in Shayne would likely also be interested in the water use of various features within the home like the toilets, faucets, washing machines, etc. in Clarke and accordingly would have modified Shayne to analyze these features.
Regarding claim 3, the combination of Shayne, Clarke and Ribbich teaches all the limitations of claim 1 and Clarke further teaches:
wherein the attributes of the property influencing the water usage of the property include one or more of a size of the property, a household size of the property, appliances of the property, climate of the property, and/or water features of the property (obtains training data for various water consumers, e.g., toilets, faucets, washing machines, etc., pg. 6, ll. 15-34).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the monitoring water of Shayne with the training process of Clarke because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized users of the system in Shayne would likely also be interested in the water use of various features within the home like the toilets, faucets, washing machines, etc. in Clarke and accordingly would have modified Shayne to analyze these features.
Regarding claim 4, the combination of Shayne, Clarke and Ribbich teaches all the limitations of claim 1 and does not explicitly teach:
updating, by the one or more processors, the historical training data set to include new data indicative of relationships between the historical water usage data set and the historical property data set; retraining, by the one or more processors, the ML model based upon the updated historical training data set; and storing, by the one or more processors, the retrained ML model on one or more memories.
Nevertheless, it would have been obvious before the effective filing date of the claimed invention to update and retain the machine learning model because duplication of parts is obvious unless a new and unexpected result is produced, see MPEP 2144.04.VI.B. That is, the combination of Shayne and Clarke teaches training the claimed machine learning for the reasons discussed above. Examiner finds no reason repeating this process to retaining the machine learning model with updated dated would produce new or unexpected results.
Regarding claim 6, the combination of Shayne, Clarke and Ribbich teaches all the limitations of claim 1 and Shayne further teaches:
wherein the notification includes actionable information comprising one or more recommendations of actions to be taken to reduce the water usage of the property based upon the water usage and the adjusted estimated water usage comparison (provides recommendations like suggesting plumber to fix broken pipe, ¶[0072]).
Regarding claim 7, the combination of Shayne, Clarke and Ribbich teaches all the limitations of claim 1 and Shayne further teaches:
wherein the notification includes one or more of an explanation of the water usage, or an access link to a water usage dashboard (report includes details such as an amount of water in an area of the property, a flow of water in an area, or a damage type (e.g., clogged gutter, broken down sprout, broken water hydrant), ¶[0068]).
Regarding claim 8, the combination of Shayne, Clarke and Ribbich teaches all the limitations of claim 1 and Shayne further teaches:
wherein obtaining the water usage data set of the property further comprises: detecting, via one or more Internet of Things (IoT) sensors, water usage data associated with an IoT sensor of the property; and receiving, by the one or more processors via the one or more IoT sensors, the IoT sensor water usage data (monitoring system includes internet connected sensors, ¶[0122]).
Regarding claim 9, the combination of Shayne, Clarke and Ribbich teaches all the limitations of claim 1 and Clarke further teaches:
wherein obtaining the water usage data set of the property further comprises: obtaining, by the one or more processors, water usage data associated with a water meter of the property (determines waterflow using meter, pg. 10, ll. 14-19).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the monitoring water of Shayne with the training process of Clarke because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized users of the system in Shayne would likely also be interested in the water use of various features within the home like the toilets, faucets, washing machines, etc. in Clarke and accordingly would have modified Shayne to analyze these features.
Regarding claims 11 and 20, claims 11 and 20 recite similar limitations as claim 1 further recite one or more processors, and one or more non-transitory memories storing processor-executable instructions; and a non-transitory computer-readable medium storing processor-executable instructions, respectively. These limitation are taught by Shayne in e.g., ¶[0124]. Accordingly claims 11 and 20 are rejected for similar reasons as claim 1.
Regarding claims 12-14 and 15-18, claims 12-18 recite similar limitations as claims 2-4 and 6-9 and accordingly are rejected for similar reasons claims 2-4 and 6-9.
Claim(s) 5, 10, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Shayne, Clarke and Ribbich further in view of Chintakindi et al, US Pub. No. 2022/0270176, herein referred to as “Chintakindi”.
Regarding claim 5, the combination of Shayne, Clarke and Ribbich teaches all the limitations of claim 1 and does not teach but Chintakindi does teach:
wherein the ML model includes an algorithm including one or more of k-nearest neighbor, support vector regression, and/or random forest (uses various techniques like support vector machine and a random forest, ¶[0050]; see also e.g., ¶[0077] discussing monitoring water usage).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the water monitoring of Shayne, Clarke and Ribbich with the machine learning techniques of Chintakindi because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have modified Shayne and Clarke to use a random forest for situations where random forests are advantageous e.g., where flexibility is needed.
Regarding claim 10, the combination of Shayne, Clarke and Ribbich teaches all the limitations of claim 1 and Shayne further teaches:
generating, by the one or more processors, a water usage dashboard including water usage information of the property (web page provides reports related to monitoring system, ¶[0115]));
and receiving, by the one or more processors, water usage validation at the water usage dashboard via the user device (user provides input confirming problems, ¶[0040]).
However the combination of Shayne, Clarke and Ribbich does not teach but Chintakindi does teach:
providing, by the one or more processors, an access link to the water usage dashboard to the user device (provides selectable link, ¶[0109]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the monitoring water of Shayne, Clarke and Ribbich with the link of Chintakindi because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have modified Shayne and Clarke to use a link to access the reports for situation where it is advantageous, e.g., to link to a web based report.
Regarding claim 19, claim 19 recites similar limitations as claim 10 and accordingly is rejected for similar reasons as claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Fan, Xudong, Xijin Zhang, and Xiong Yu. "Machine learning model and strategy for fast and accurate detection of leaks in water supply network." Journal of Infrastructure Preservation and Resilience 2.1 (2021): 10 teaches a similar analysis
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 10-6.
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626
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