DETAILED ACTION
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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 03/13/2026, Applicant, on 06/12/2026, amended claims. Claims 1-20 are pending in this application and have been rejected below.
Response to Arguments
Applicants’ arguments filed 06/12/2026 have been fully considered, but they are not fully persuasive. The updated 35 USC § 112(a), 112(b), and 103 rejections of claims 1-20 are applied in light of Applicant's amendments.
The Examiner has reviewed the arguments and does not find them persuasive. The arguments with respect to claim 1 are moot in view of the new grounds of rejection set forth below, which necessitated by Applicant’s amendments. The new grounds do not rely on the references applied in the prior rejection of record for the matter challenged in Applicants arguments; the deploying limitation is now taught by Pardell rather than Bain, and Bain is relied upon only for generating the set of forecasts using the first AI engine. Regarding any arguments directed to limitations that were not amended, those arguments have been fully considered but are not persuasive, and the rejection is maintained. Applicant is further directed to the rejections under 35 USC § 112(a) and 112(b) which were necessitated by the amendments made.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 11, and 20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims recite “with (i) the at least one energy production device and (ii) the energy production equipment both being in a common operational state during the operation by the at least one physical system.” Neither the term “operational state” nor the word “state” appears in the specification. The spec states “operational data” and “operational parameters”, neither of which describe any common state between energy production device and energy production equipment. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
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, 11, and 20 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.The claim(s) recite “ at the at least one energy production device to cause the at least one energy production device to output more energy with (i) the at least one energy production device and (ii) the energy production equipment both being in a common operational state during the operation by the at least one physical system.” It is unclear what is being claimed. The energy production device is claimed as a component of the energy production equipment, it is unclear how the device and the equipment are to be distinguished from one another for purposes of determining whether they occupy a “common” state.
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 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.
Claims 1-5, 7, 11-15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20170210470 (hereinafter “Pardell”) et al., in view of U.S. PGPub 20190372345 to (hereinafter “Bain”) et al.
As per claim 1, Pardell teaches: a computer-implemented method of optimizing energy produced by an energy production site, said method comprising:
receiving, by at least one processor from an energy sensing system deployed at the energy production site, data indicative of real-time dynamic energy production of energy production equipment including at least one enemy production device at the energy production site; generating, using a first artificial intelligence engine, a set of forecasts related to the energy produced by the energy production site including at least one of (i)
power generation, (ii) market price, (iii) market demand, and (iv) useful life of the energy production equipment; Pardell 0007: “Our invention objective is to implement a low cost solution for the automated cleaning of large outdoors glazed areas. Its primary application is for the cleaning of large areas of PV (photovoltaic) panels and heliostats in solar plants, but it can also be used in other applications, like glass windows on commercial buildings.” 0087: The art teaches the ability to obtain complementary data from internet: weather and solar radiation forecasts, satellite pictures, etc.
automatically determining…underperformance of the energy production site by performing at least one of (i) forecasting energy production, (ii) determining actual versus expected energy production, and (iii) monitoring a common piece of equipment across each of a plurality of parallel branches of common energy production equipment; Pardell 0003: In CSP, heliostat reflectivity is particularly affected by soiling, as the direct beam is dispersed twice by dirt particles. For the same reason, CPV systems using reflective primary concentrators are also more affected by soiling than refractive CPV systems.
in response to determining underperformance of the energy production site, automatically selecting an inspection system from amongst a plurality of available inspection systems configured to (i) perform inspection of the energy production site and (ii) generate data captured at the energy production site; Pardell 0090: Three kinds of primary missions can be assigned to drones: cleaning, recognition and transport. Other secondary missions (not directly related to cleaning) can be assigned, like module inspection, IR analysis or perimeter surveillance.
automatically analyzing, by the at least one processor, the data captured from the selected inspection system to produce inspection analysis data;Pardell 0139, FIG. 1: Finally, ground control system 3 could also use clean drones 1 to execute other kind of secondary missions, like perimeter security surveillance, module/mirror inspection or IR imaging of PV modules in order to locate hot spots.
determining, by the at least one processor, whether or not to perform a remedial action to increase energy production by the energy production equipment at the energy production site by executing an optimization engine that utilizes a function of the (i) set of forecasts generated by the first Al engine, (ii) inspection analysis data, and (iii) one or more current and forecasted environmental factors at the energy production site;Pardell 0088-0101: “Determines cleaning needs using images obtained from drones in recognition missions, solar plant monitoring, climatologic history, weather forecast and other information resources…The ground control system will decide when the conditions exist to schedule cleaning operations, using long term scheduling criteria and a combination of short term local meteorological data and other relevant information, like weather forecasts or historical plant electrical production data.”
deploying, based on results of the optimization engine that…;Pardell 0101-0102: “The ground control system will decide when the conditions exist to schedule cleaning operations, using long term scheduling criteria and a combination of short term local meteorological data and other relevant information, like weather forecasts or historical plant electrical production data. Then it will assign cleaning missions to cleandrones, each time assigning a specific area of solar panels or heliostats to be cleaned by each drone. The usual cleandrone operational cycle will follow this schema: Ground control system assigns a cleaning mission to a cleandrone. With buffer battery charged, cleandrone flies to a docking station holding a ready cargo pack (main battery pack fully charged, cleaning solution tank full, waste tank empty) and picks it up. Then it flies to assigned solar panel area and executes cleaning procedure. When cleaning mission is finished, cleandrone flies back to a free docking station, drops empty cargo pack, and if another mission is assigned, flies again to a ready docking station to retrieve another fresh cargo pack.”
the remedial action to cause at least one physical system independent of the energy production equipment to perform an operation at the at least one energy production device to cause the at least one energy production device to output more energy with (i) the at least one energy production device and (ii) the energy production equipment both being in a common operational state during the operation by the at least one physical system at the energy production site if a determination to perform remedial action is made, the energy production site increasing energy production in response to the at least one physical system performing the operation on the at least one energy production device; Pardell 0009: “The core of this invention consists of a robotic small flying drone able of controlled vertical flight, based on rotating aerofoils as lifting means and having pusher propellers for improved horizontal flight and pushing/pulling force, and to which a glass cleaning device is attached. Also, an accurate 3d positioning system and optical recognition cameras and proximity sensors are integrated. We will thereafter refer to this specialised kind of drone as a cleandrone.0103: For vertical surfaces the cleaning procedure is initiated in the following way: Drone arrives to the assigned solar panel area based on positioning system coordinates. It conducts a visual recognition of the area in order to identify the initial glass panel assigned. After this, the following actions go in sequence: Cleandrone flies to initial glass panel at low speed and it hovers in front of glass panel using visual recognition of panel frame.0002: “ Productivity of solar energy systems is negatively affected by soiling. In solar energy conversion, the loss mechanisms related to soiling are basically two: absorption and dispersion of photons in dust or dirt particles covering the surface of solar collectors.0105: During the cleaning phase, the cleandrone will fly downwards until it reaches the glass panel bottom, executing a continuous cleaning stroke by simultaneously doing the following actions: 1. Spray cleaning solution at high pressure. This wets the surface and loosens the dirt 2. Water squeezing and recovery through suction system.0089: Schedules and programs cleaning operations. A solar field for instance can be divided in areas, and cleaning operations can be rotated between the different areas.102: Then it flies to assigned solar panel area and executes cleaning procedure. When cleaning mission is finished, cleandrone flies back to a free docking station, drops empty cargo pack, and if another mission is assigned, flies again to a ready docking station to retrieve another fresh cargo pack.”
Pardell may not explicitly teach the following. However, Bain teaches:
by the at least one processor, based on the set of forecasts generated by the first Al engine …; at least in part utilizes the set of forecasts generated by the first AI engine…;Bain 0341-0465: “The data repository 102 may also connect to a demand management engine 110. To manage demand via the demand management engine 110, a machine learning engine 104 may derive insights based on and related to various characteristics that may affect demand, such as time of day, season, geography, generator source distance to a consumer premises, gamification patterns, price patterns, production patterns, weather patterns, user behavioral information based on individual usage patterns and the like. These factors may allow identification of combinations of pricing, points, messages, and other factors that may affect demand… This may include optimizing rooftop solar generation usage based on real-time retail energy market information. Because solar output may be favorable in the platform, it may make sense for consumers to optimize usage of rooftop solar generation at the home (or other local production capabilities, such as small-scale wind, geo-thermal and hydro-power), where consumption is behind the meter, rather than exporting energy to the grid, depending on price signals and current/projected usage…The various embodiments disclosed herein produce data that can be mined for a myriad of value-producing purposes, including device design by manufacturers, device selection and replacement at the consumer's home, grid management by the ISOs, and power station optimization…the marketplace platform may enable managing production based on the forecast demand for energy from particular types of energy sources. The marketplace platform may collect and optionally aggregate demand estimates for each of the raw sources of energy from a collection of consumers, such as indicated by consumers in a mobile application or other interfaces of the platform. An energy producer load manager may control or signal for energy flow from the raw energy sources at least in part based on the demand.”
Pardell and Bain are deemed to be analogous references as they are reasonably pertinent to each other and directed towards deploying, collecting, and analyzing information and machines to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Pardell with the aforementioned teachings from Bain with a reasonable expectation of success, by adding steps that allow the software to utilize increasing production with the motivation to more efficiently and accurately deploy energy [Bain 0494].
As per claim 2, Pardell and Bain teach all the limitations of claim 1.
In addition, Pardell teaches:
wherein selecting an inspection system includes selecting a visual inspection;Pardell 0103: For vertical surfaces the cleaning procedure is initiated in the following way: Drone arrives to the assigned solar panel area based on positioning system coordinates. II conducts a visual recognition of the area in order to identify the initial glass panel assigned.
As per claim 3, Pardell and Bain teach all the limitations of claim 1.
In addition, Pardell teaches:
wherein receiving data indicative of real-time dynamic energy production includes receiving data indicative of solar power generated energy;Pardell 0007: For large solar farms the ideal conditions exist to implement a completely automated cleaning operation, because access is restricted, the area is fenced, human presence is scarce, and topography and glass surfaces are well charted.
As per claim 4, Pardell and Bain teach all the limitations of claim 3.
In addition, Pardell teaches:
wherein automatically selecting an inspection system includes automatically selecting a drone configured to fly over a solar farm to capture images of solar panels of the solar farm;Pardell 0001: The present Invention refers to cleaning drones, docking stations configured to operate with the cleaning drones, systems or installations comprising one or more cleaning drones, one or more docking stations and at least one ground control system controlling the cleaning operations, as well as use of the cleaning drones in methods for cleaning surfaces such as photovoltaic panels or windows for example.
As per claim 5, Pardell and Bain teach all the limitations of claim 1.
In addition, Pardell teaches:
wherein deploying the remedial action includes deploying a solar panel cleaning system;Pardell 0022: The A further aspect of the invention refers to a cleaning system comprising one or more cleaning drones according to the invention, one or more docking stations according to the invention and a ground control system, said control system being configured to read charging status, current position, and other relevant information from said drones and docking stations, and being configured to send cleaning mission commands to said drones and to coordinate recharging, refueling, emptying and reloading operations between said drones and said docking stations.
As per claim 7, Pardell and Bain teach all the limitations of claim 1.
In addition, Pardell teaches:
generating a control signal to alter at least one of the common pieces of equipment;Pardell 0100: Operations can be fully automated, semi-automated or manual. For more complex situations a human operator may guide the drone during the translation and initial positioning of the drone on the glass panel. For large solar farms, the conditions exist to allow for a fully automated clean drone operation which will be completely directed by the ground control system without requiring human intervention.
Claims 11-15, 17, and 20 are directed to the system for performing the method of claims 1-5 and 7 above. Since Pardell and Bain teach the system, the same art and rationale apply.
Claims 6, 8, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20170210470 (hereinafter “Pardell”) et al., in view of U.S. PGPub 20190372345 to (hereinafter “Bain”) et al., in further view of U.S. PGPub 20190230850 to (hereinafter “Johnson”) et al.
As per claim 6, Pardell and Bain teach all the limitations of claim 1.
Pardell and Bain may not explicitly teach the following. However, Johnson teaches:
wherein deploying the remedial action includes deploying an automated mowing system;Johnson 0068: “The autonomous lawn mowers 100 may access the loading elevator 502 via the loading/unloading ramp 505. In some embodiments, the loading/unloading ramp 505 may be automatically deployed when an autonomous lawn mower 100 approaches the service vehicle 500. For example, the autonomous lawn mower 100 may transmit a signal to the controller 512 on the service vehicle 500 indicating that the ramp is to be lowered. This signal may further cause the controller to activate the loading elevator 502 to prepare to move the autonomous lawn mower 100 to an open charging/storage bay 504. In some configurations the autonomous lawn mower 100 may be configured to automatically drive up onto the loading/unloading ramp 505. In other embodiments, a user may manually lift the autonomous lawn mower up the loading/unloading ramp 505 to the loading elevator 502. In some configurations, the loading/unloading ramp 505 may be manually controlled by a user, such as by activating a switch or other control device, or by manually lowering and raising the ramp via a mechanical mechanism. In some embodiments, the loading/unloading ramp 505 may be automatically raised and lowered using various configurations. For example, an electric motor, hydraulic actuators, linear electric actuator, pneumatic actuators, and the like may be utilized to raise and lower the loading/unloading ramp.”
Pardell, Bain, and Johnson are deemed to be analogous references as they are reasonably pertinent to each other and directed towards deploying, collecting, and analyzing information and machines to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Pardell and Bain with the aforementioned teachings from Johnson with a reasonable expectation of success, by adding steps that allow the software to utilize a mowing machine with the motivation to more efficiently and accurately deploy a machine [Johnson 0068].
As per claim 8, Pardell and Bain teach all the limitations of claim 1.
Pardell and Bain may not explicitly teach the following. However, Johnson teaches:
generating a control signal to alter an inverter;Johnson 0047: “In response to determining that the roll angle is greater than the first predefined amount, the operating/control module 420 can turn off chore motors (or cause the motor controllers to turn off chore motors). However, the operating/control module 420 may continue to operate the drive motors so that the operator of the mower 100 can correct the dangerous situation. If the operating/control module 420 determines that the roll angle is greater than both the first predefined and a second predefined amount greater than the first predefined amount, this may indicate that the mower 100 is flipping or rolling, has flipped or rolled, or is very likely to flip or roll. In this regard, the operating/control module 420 can be configured to shut down all motors and/or apply braking devices…0060: the safety module 432 may receive data indicating that the autonomous lawn mower 100 may be in danger (e.g. fall off an edge, overturn, etc.) based on one or more sensed or determined parameters. The safety module 432 may then execute certain safety functions to protect one or more systems within the autonomous lawn mower. Safety functions may include shutting off power to one or more systems within the autonomous lawn mower 100. Further safety systems may include deploying impact reduction devices (e.g. airbags, bumpers, etc.) based on the safety module 432 determining that there may be an impact or collision affecting the autonomous lawn mower 100.”
Pardell, Bain, and Johnson are deemed to be analogous references as they are reasonably pertinent to each other and directed towards deploying, collecting, and analyzing information and machines to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Pardell and Bain with the aforementioned teachings from Johnson with a reasonable expectation of success, by adding steps that allow the software to utilize a power modes with the motivation to more efficiently and accurately deploy a machine [Johnson 0060].
Claims 16 and 18 are directed to the system for performing the method of claims 6 and 8 above. Since Pardell, Bain, and Johnson teach the system, the same art and rationale apply.
Claims 9-10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20170210470 (hereinafter “Pardell”) et al., in view of U.S. PGPub 20190372345 to (hereinafter “Bain”) et al., in further in view of U.S. Patent 11153496 to (hereinafter “Wang”) et al.
As per claim 9, Pardell teaches all the limitations of claim 1.
Pardell may not explicitly teach the following. However, Wang teaches:
wherein automatically analyzing, by the at least one processor, the data captured from the selected inspection system to produce inspection analysis data includes executing, by the at least one processor, an artificial intelligence engine to automatically identify abnormalities captured in images or videos by the selected inspection system;Wang 0014: “ processor 142 may obtain the defect type DT of the thermal abnormality condition corresponding to the solar modules SLM1 to SLM4 from the database 170 through artificial intelligence (AI)…0027:The solar module detection system may capture the visible light image and the thermal image of the solar module along the moving path. In this way, when the solar module is in operation, time for capturing the visible light image and the thermal image may be reduced. The solar module detection system determines the defect type of the thermal abnormality condition of the thermal image by using at least the visible light image. In this way, the solar module detection system may improve accuracy of solar module detection. Moreover, the solar module detection system may further determine the defect type of the thermal abnormality condition in the thermal image through at least one of user operation and AI.”
Pardell, Bain, and Wang are deemed to be analogous references as they are reasonably pertinent to each other and directed towards deploying, collecting, and analyzing information and machines to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Pardell and Bain with the aforementioned teachings from Wang with a reasonable expectation of success, by adding steps that allow the software to utilize a imaging software with the motivation to more efficiently and accurately analyze information [Wang 0027].
As per claim 10, Pardell and Wang teach all the limitations of claim 9.
Pardell may not explicitly teach the following. However, Wang teaches:
wherein automatically identifying abnormalities includes identifying at least one of (i) cracks on a solar panel, (ii) hotspots on a solar panel, or (iii) shadows on a solar panel;Wang 0014: “ processor 142 may obtain the defect type DT of the thermal abnormality condition corresponding to the solar modules SLM1 to SLM4 from the database 170 through artificial intelligence (AI)…0022: the processor 142 may determine that a shading object (for example, a lightning conductor or an antenna) exists in the visible light image VIMG at a location corresponding to the thermal abnormality condition AB1. Therefore, the processor 142 determines that the thermal abnormality condition AB1 is not a defect in the module, but is a hot spot caused by a shade.”
Pardell, Bain, and Wang are deemed to be analogous references as they are reasonably pertinent to each other and directed towards deploying, collecting, and analyzing information and machines to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Pardell and Bain with the aforementioned teachings from Wang with a reasonable expectation of success, by adding steps that allow the software to utilize a imaging software with the motivation to more efficiently and accurately analyze information [Wang 0022].
Claim 19 are directed to the system for performing the method of claim10 above. Since Pardell, Bain, and Wang teach the system, the same art and rationale apply.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/
Primary Examiner, Art Unit 3625
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