DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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 Information Disclosure Statements, filed 28 June 2024; 15 August 2024; 25 February 2025; 05 May 2025; 27 June 2025; 08 August 2025; 09 September 2025; and 08 December 2025; have been fully considered by the examiner. Signed copies are attached.
Acknowledgement is made of the corrected drawings filed on 24 May 2024, and the application is being examined on the basis of the amended disclosure.
Claims 1-20 are pending.
Claims 1-20 are rejected, grounds follow.
Drawings
The replacement drawings were received on 24 May 2024. These drawings are Objected to.
The drawings are objected to because of the quality of the lines and characters. 37 CFR 1.84(l) requires that all drawings must be made by a process which will give them satisfactory reproduction characteristics. Every line, number, and letter must be durable, clean, black (except for color drawings), sufficiently dense and dark, and uniformly thick and well-defined. Examiner asserts that at least the lines other than the diamonds and chevrons of fig. 1A, 1B, and the interior lines of the perspective drawing of Fig. 2C do not meet this standard. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Examiner notes for the record that color drawings were submitted however the fee for color drawings does not appear to have been paid. Accordingly the above rejection is based on the black and white reproduction of said drawings.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lekouch et al., "Rooftop dew, fog and rain collection in southwest Morocco and predictive dew modeling using neural networks." Journal of Hydrology 448 (2012): 60-72 (citations to original pagination; IFW copy furnished by applicant 27 June 2025) in view of Friesen et al., US Pg-Pub 2020/0332498 (hereafter Friesen’498).
Regarding Claims 1, 12 and 19, Lekouch teaches:
A computer-implemented method (i.e. neural networks, see title) comprising: receiving, by one or more processors, one or more optimization inputs (Page 63, left hand column 178 dew events were recorded” from leaf wetness sensors, see page 62 section 2.4 and visual confirmation, ibid.) for a time (Page 63, 1-year study from May 2007 to April 2008) and a location (ibid. Mirleft, Morocco) associated with an operation of an atmospheric water generation system; (Dew Collectors and Fog Collectors, see Pages 61-62, sections 2.1 and 2.2).
generating, by the one or more processors and using an optimization machine learning model, (see Page 67, section 6.0; “Another possibility is to study the correlations between the condensation volume and meteorological parameters. A model can be built using an artificial neural network (ANN).”) one or more time-based energy predictions (page 65, section 5.1, e.g. available cooling energy, see left hand column and eqs. 1-3.) for the atmospheric water generation system (e.g. the condensers, ibid.) based at least in part on the one or more optimization inputs; (Page 65: “In the present study, one indeed observes that condensation mostly occurs between RH = 74% and 92% (Fig. 8). The lower limit corresponds to the maximum possible cooling with the available energy and the upper limit representing the formation of fog that limits the cooling energy.”)
Lekouch differs from the claimed invention in that:
Lekouch does not appear to clearly articulate: communicating, by the one or more processors, one or more control instructions to one or more controllers of the atmospheric water generation system to initiate one or more atmospheric water generation operations based at least in part on the one or more time-based energy predictions.
(that is, where Lekouch is a passive collection system with collectors placed according to the AI model so that collection of atmospheric water is maximized (see Lekouch summary); the claimed invention uses an active collector where atmospheric conditions are brought into an optimum by active control surfaces.)
However, the use of Active collectors steered by machine learning is well known, as exemplified by Friesen’498 which teaches: a machine learning based system ([0039] “the control system can use… an onboard deterministic and/or machine learning algorithm”) which communicates one or more control instructions (e, g, varying exchange rate of an enthalpy exchange unit, see [0043]) to one or more controllers (ibid. [0043] “the enthalpy exchange rate can be varied by controller 160 in wired or wireless communication with enthalpy exchange unit 140”) of an atmospheric water generation system ([0025] “FIG. 1 depicts a water generation system 100 for generating liquid water from a process gas containing water vapor, for example ambient air.” ) in order to initiate an operation ([0038] “(e.g., by adjusting rate of rotation for a rotary desiccant), the flow rate of the working fluid in the working fluid pathway (e.g. via fan 116) or a combination thereof.”) of said system responsive to a time-based energy prediction ([0039] “The control system can dynamically maximize the production of liquid water over the diurnal cycle based on current or forecast ambient conditions (e.g. solar insolation, ambient temperature, ambient humidity), current or forecast system properties (e.g. working fluid temperature, working fluid humidity, water content of hygroscopic materials of the system). The control system can use a set of sensors, an onboard deterministic and/or machine learning algorithm, information regarding the thermodynamics of water vapor, information regarding the properties of the hygroscopic materials, information regarding the amount of liquid water produced, information regarding the amount of water vapor retained by the thermal desiccant unit, and/or other factors that can be synthesized in the controller to optimize water production at the condenser.”)
Friesen’498 is analogous art because it is from the same field of endeavor as the claimed invention and other references of atmospheric water generation systems;
Accordingly, examiner finds 1) the prior art contained a device (method, product, etc.) which differed from the claimed device by the substitution of some components (step, element, etc.) with other components – the method of Lekouch, which differed by the substitution of an active AGW system and associated control instructions for the passive collector of Lekouch; 2) the substituted components and their functions were known in the art – as exemplified by Friesen’498 which teaches an active AGW system which is controlled by a controller implementing a machine learning function; 3) one of ordinary skill in the art before the effective filing date of the application could have substituted one known element for another, and the results of the substitution would have been predictable at least because Friesen’498 teaches that the system of Friesen is suitable for harvesting water from ambient air and the method of Lekouch is applicable to systems that harvest water from ambient air; and accordingly the substitution would have been obvious to one having ordinary skill in the art before the effective filing date of the application (See MPEP 2143.I.B)
Regarding Claims 12 and 19, these claims recite substantively the same subject matter, except embodied as a computing system and an atmospheric water generation system, respectively; mutatis mutandis these claims are likewise obvious over Lekouch in view of Friesen’498 for the same reasons articulated with respect to claim 1.
Regarding Claims 2 and 13, Lekouch in view of Friesen’498 teaches all of the limitations of parent claims 1 and 10 respectively,
Lekouch further teaches:
wherein the optimization machine learning model is previously trained, using one or more supervisory training techniques, (see page 67 discussing architecture and page 68, e.g. “Having chosen the architecture of the network, the calculations of weights and bias were optimized by a correct division of the data set. In practice, sensitivity to data distribution was reduced by choosing for the training set the first 240 days of the data set (from 1 May 2007 to 26 December 2007) because this period is representative of the range of dew yields that can be encountered over the year. Once the training phase was achieved, the performance of the network was validated on an independent validation set (i.e. the remaining 125 days to 30 April, 2008).”)
based at least in part on a training dataset comprising a plurality of labeled optimization training entries (see figs. 7-12, depicted labeled data from the sensors, which was divided into the training and validation data sets, see page 67 cited supra.)
and each of the plurality of labeled optimization training entries comprises a set of historical optimization inputs (see page 68 “Following the analysis carried out in the previous sections of the influence of each meteorological parameter on dew yield, we chose as input parameters: Ta (ambient air temperature), RH (relative humidity), V (wind speed) and N (cloud cover).”)
and historical performance data (see page 65, “Dew yield (h)”, e.g. eq (3) and figs. 8-10) corresponding to the set of historical optimization inputs. (see figs. 8-10 showing the correspondence between recorded dew yield (h) and the respective meteorological parameters.)
Regarding Claims 3 and 14, Lekouch in view of Friesen’498 teaches all of the limitations of parent claims 2 and 13, respectively;
Lekouch further teaches:
wherein the historical performance data is indicative of a ground truth water output from the atmospheric water generation system based at least in part on one or more historical atmospheric water generation operations. (see figs. 8-10, historical data includes actual (i.e. ground truth) measured dew yields (h) corresponding to the measured historical atmospheric conditions.)
Regarding Claim 4, Lekouch in view of Friesen’498 teaches all of the limitations of parent claim 2;
Lekouch further teaches:
receiving, by the one or more processors, energy usage data and performance data corresponding to the one or more atmospheric water generation operations; (see e.g. fig. 8, and eqs. 1-3 on page 65, relating dew yields (atmospheric water generation operations) to the available energy low and upper cooling limits.)
and storing, by the one or more processors, the one or more optimization inputs, the energy usage data, and the performance data as a labeled optimization training entry in the training dataset. (see sections 2.1 – 2.4 describing data collection methodology. Data was collected and labeled over a period of one year for dew yield, rain yield, fog yield, and corresponding meteorological data, then entered into a training data set (see 6.2) for the neural network.)
Regarding Claim 5, Lekouch in view of Friesen’498 teaches all of the limitations of parent claim 4,
Lekouch further teaches:
retraining, by the one or more processors, the optimization machine learning model based at least in part on the labeled optimization training entry. (see page 68, section 6.2 “For a given number m of hidden neurons, the algorithm was repeated a sufficient number of iterations until the MSE was minimized.”)
Regarding Claims 6 and 15, Lekouch in view of Friesen’498 teaches all of the limitations of parent claims 1 and 12, respectively;
Friesen’498 further teaches:
generating, by the one or more processors, an optimized energy output for the atmospheric water generation system based at least in part on the one or more time-based energy predictions ([0040] “Controller 160 can operate the system 100 to vary the exchange rate of the enthalpy exchange unit 140 based on an ambient solar flux, an ambient temperature, an ambient relative humidity, a temperature of the working fluid, a relative humidity of the working fluid, an amount of water present in the hygroscopic material 120, an elapsed time, a user input and so on.” See also rejection of claim 1 in combination with Lekouch, supra.) and water usage data for the location corresponding to the atmospheric water generation system; ([0042] “Furthermore, controller 160 can be programmed or configured to optimize liquid water production based on inputs relating to… Liquid water usage rate”)
and generating, by the one or more processors, the one or more control instructions based at least in part on the optimized energy output. (ibid. see also [0043] e.g. “the enthalpy exchange rate can be varied by controller 160 in wired or wireless communication with enthalpy exchange unit 140”)
Regarding Claims 7 and 16, Lekouch in view of Friesen’498 teaches all of the limitations of parent claims 6 and 15, respectively;
Friesen’498 further teaches:
wherein the water usage data is based, at least in part, on user input or sensor data ([0042] “Furthermore, controller 160 can be programmed or configured to optimize liquid water production based on inputs relating to system operational parameters like … liquid water usage rate.”) from the atmospheric water generation system. ([0041] “Controller 160 can be associated with peripheral devices (including sensors) for sensing data information, data collection components for storing data information, and/or communication components for communicating data information relating to the operation of the system.”)
Regarding Claims 8 and 17, Lekouch in view of Friesen’498 teaches all of the limitations of parent claims 1 and 12, respectively;
Friesen’498 further teaches:
wherein the one or more optimization inputs comprise sensor data from the atmospheric water generation system. ([0041] “Controller 160 can be associated with peripheral devices (including sensors) for sensing data information, data collection components for storing data information, and/or communication components for communicating data information relating to the operation of the system.”)
Regarding Claims 9 and 18, Lekouch in view of Friesen’498 teaches all of the limitations of parent claims 8 and 17, respectively;
Friesen’498 further teaches:
wherein the one or more optimization inputs comprise current weather data or prospective weather data from one or more external informational sources. ([0042] “Controller 160 can be programmed or configured to optimize liquid water production based on measurements of one or more inputs (e.g., such that controller 160 may optimize liquid water production based on current or expected environmental and system conditions) including but not limited to external conditions like ambient air temperature, ambient pressure, ambient air relative humidity, solar insolation, solar flux, weather forecast”)
Regarding Claims 10 and 20, Lekouch in view of Friesen’498 teaches all of the limitations of parent claims 1 and 19, respectively;
wherein the atmospheric water generation system is associated with a cluster of a plurality of connected atmospheric water generation systems, ( e.g. Mirleft and Id Ouasskssou, see page 61 section 1 left hand column.) each of the plurality of connected atmospheric water generation systems are associated with a different location, (page 61 “the first was Mirleft… with four 1 m2 condensers and a 1 m2 fog collector. The second was at Id Ouasskssou… with a single 2 m2 condenser and a 1 m2 fog collector.”)
and the one or more optimization inputs comprise remote sensor data from each of the plurality of connected atmospheric water generation systems. (Page 62, 2.4 e.g. “At Mirleft, meteorological data (air temperature Ta, relative
humidity RH, dew point temperature Td, wind speed V, wind direction) and water from the rain gauge were collected every 15 min using a wireless weather station (DAVIS, Vantage pro II), located close to the edge of the building,” a species of remote meteorological sensor data package.)
Regarding Claim 11, Lekouch in view of Friesen’498 teaches all of the limitations of parent claim 1,
Friesen’498 further teaches:
providing, by the one or more processors, the one or more control instructions to an edge device (e.g. enthalpy exchange unit 140; fan 116;) that is (i) physically disposed on the atmospheric water generation system ([0035] “The water generation system 100 can further comprise an enthalpy exchange unit 140 operatively coupled between the thermal desiccant unit 102 and the condenser 130.” [0032] “System 100 can further include one or more working fluid blowers or fans 116 to increase or adjust the flow rate of the working fluid into the thermal desiccant unit 102.”) and (ii) electrically connected to at least one of the one or more controllers of the atmospheric water generation system. (see fig. 1, control lines from Controller 160 to Fan 116, Enthalpy unit 140, etc. see also [0043] describing Controller 160 in either wired or wireless communication with components 116, 140, etc.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lake et al., US Pg-Pub 2023/0072631 – particularly for background passages describing labeling of training data as standard practice in training machine learning algorithms.
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/J.T.S./Examiner, Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119