DETAILED ACTION
This is the first Office Action regarding application number 18/964,318.
This action is in response to the Applicant’s Response received 05/22/2026.
Status of Claims
Claims 1-20 are currently pending.
Claim 8 is amended.
Claims 1-20 are examined below.
No claim is allowed.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over KIM (KR 2020-0057822 A).
Regarding claims 1, 8, and 15, KIM teaches a method comprising:
receiving temperature data of a surface of a solar panel from a sensor (“panel temperature” para. 47);
receiving energy output data of the solar panel (“voltage and power values per panel”, para. 47);
executing an artificial intelligence (Al) model on the temperature data and the energy output data to generate an output from the Al model which includes a predicted point in time at which the solar panel is to be cooled (para. 51, deep reinforcement learning method, a type of AI; “the degree to which solar power generation modules are cooled through watering must be predicted”, para. 56); and
activating a cooling mechanism at the predicted point in time, wherein the cooling mechanism is configured to cool the solar panel (para. 51).
Although KIM does not explicitly state that the “predicted point in time” is part of the generated output, the examiner determines that skilled artisans would understand KIM’s teaching of “the degree” of cooling would include both when to being cooling and for how long said cooling must continue, and when it must end, and that this concept would be considered obvious to skilled artisans as a matter of basic logic and common sense.
Regarding claims 2, 9, 16, KIM teaches the method of claim 1 (etc), comprising receiving ambient temperature data from a temperature sensor of an environment around the solar panel, wherein the executing comprises predicting the point in time based on execution of the Al model on the ambient temperature data (para. 47).
Regarding claim 3, 10, 17, KIM teaches the method of claim 1 (etc), comprising receiving weather forecast data for a geographic area that includes the solar panel from an external data source, wherein the executing comprises predicting the point in time based on execution of the Al model on the weather forecast data (weather data collected through open APIs provided by forecasting agencies, para. 46).
Regarding claim 4, 11, 18, KIM teaches the method of claim 1 (etc), comprising predicting an amount of water for the cooling mechanism to use based on execution of the Al model on the temperature data and the energy output data and controlling the cooling mechanism to use the amount of water (“the analysis unit may include a sprinkler control operating cost calculation unit that calculates the operating costs required to provide sprinkler for cooling by verifying the flow rate”, para. 16).
Regarding claim 5, 12, 19, KIM teaches the method of claim 1 (etc), comprising predicting a period of time for the cooling mechanism to be active, and deactivating the cooling mechanism at an expiration of the predicted period of time (“the control unit (330) performs various spray-related controls (pump operation, opening and closing of valves, spray time” para. 51).
Regarding claim 6, 13, 20, KIM teaches the method of claim 1 (etc), comprising capturing additional energy output data from the solar panel after activating the cooling mechanism, and retraining the AI model based on the point in time, the temperature data, the energy output data, and the additional energy output data (“retraining” is interpreted to read on para. 58 describing the that reward information is “refined” by the analysis unit).
Regarding claim 7, KIM teaches the method of claim 1 (etc), wherein the cooling mechanism comprises a nozzle configured to spray water on the surface of the solar panel, and the activating comprises activating the nozzle to spray water on the surface of the solar panel based on the output of the AI model (nozzle selection, para. 51).
Conclusion
No claim is allowed.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELO TRIVISONNO whose telephone number is (571) 272-5201 or by email at <angelo.trivisonno@uspto.gov>. The examiner can normally be reached on MONDAY-FRIDAY, 9:00a-5:00pm EST. The examiner's supervisor, NIKI BAKHTIARI, can be reached at (571) 272-3433.
/ANGELO TRIVISONNO/
Primary Examiner
Prosecution Insights