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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/02/2025 has been entered.
Response to Arguments
Applicant's arguments filed 12/02/2025 have been fully considered, but they are not fully persuasive. The updated 35 USC § 103 rejection of claims 1-20 are applied in light of Applicant's amendments.
The Applicant argues “Pardell and Bain fail to teach each and every element of amended independent claims 1, 11, and 20, from which claims 9-10 and claim 19 respectively depend.” (Remarks 12/02/2025)
In response, the Examiner respectfully disagrees. The combination of Pardell and Bain teach all the limitations of claim 1. The Applicant amended claim to include “automatically determining, by the at least one processor, based on the set of forecasts generated by the first Al engine … deploying, based on results of the optimization engine that, at least in part utilizes the set of forecasts generated by the first Al engine, the remedial action to cause at least one physical system independent of the energy production equipment to perform an operation.” Bain teaches AI/machine learning prediction engine forecasting energy producing, prices, and demand (see Bain 0332-0339); while Pardell teaches the ability to determine cleaning needs based on weather forecasts and historical plant production data (see Pardell 0088 and 0101). Bain also teaches determining insights based on production and weather patterns for demand management; along with real time market information and predictions, forecast demands (an optimization engine set to forecast by AI/ML). The combination also teaches the ability to deploy physical systems (Pardell 0042, 0101) based on machine learning forecasts (Bain 0390-0393); and the ability to increase energy production (see Bain 0490). Thus, one of ordinary skill in the art would agree that the combination of Bain and Pardell teach all the limitations of claim 1.
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 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 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.”
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 … 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.”
deploying, based on results of the optimization engine that, at least in part utilizes the set of forecasts generated by the first Al engine, the remedial action to cause at least one physical system independent of the energy production equipment to perform an operation 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 physical system performing the operation; Bain 0390-093: “optimize energy consumption. For example, given that a price signal is handled in the platform 100, an extension of the platform 100 may provide control of devices 198 in a user's home remotely to react to price signals. This may occur in the mobile application 122 by a device control interface 190 or automatically under control of the platform 100… in which case electricity use control can be important, including cases where the user has its own energy generation (e.g., solar)…. a smart home system, such as a system that enables interaction among and control of home systems, such as energy-related systems (e.g., heating, air conditioning, ventilation, sunlight control, and the like) may interface with an energy marketplace platform to enable automated demand management… platform to coordinate with smart home systems and the like to automatically adjust energy demand. By signaling to a home control system when energy costs are low/high or when distributed energy from raw renewable energy is or is predicted to be available, a home system may adjust demand of energy consuming systems in or associated with the home. In an example of automated demand management… 0490-0494: “In embodiments, notification to behind-the-meter devices may include notification to a renewable energy production system under the control of a consumer, such as a solar energy production system to increase production of electricity even though demand for electricity behind-the-meter is being met and to return excess produced electricity to the grid… This changed/new demand may be communicated from the platform to energy providers who may adjust production capacity and/or pricing based on the demand. If production capacity is available, production capacity may be increased in response to increasing demand. If production capacity is not available, prices for the energy may be increased in response to the increasing demand. Providing a feedback system that is integrated with pricing notification may include estimating a demand for a raw energy-specific consumer energy-type by calculating energy usage for consumers who have indicated a desire to consume energy produced from a specific raw energy type.”
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:
wherein deploying the remedial action includes 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:
wherein deploying the remedial action includes 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
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