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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/22/2026 has been entered.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2021-0176926, filed on 12/10/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/11/2023, 06/20/2023, 03/04/2024, 01/16/2025, and 09/03/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed 03/04/2026regarding the rejection under 35 USC 112(a) have been fully considered and are persuasive.
The rejection under 35 USC 112(a) is withdrawn.
Applicant's arguments filed 03/04/2026regarding the rejection under 35 USC 101 have been fully considered and are persuasive.
The rejection under 35 USC 101 is withdrawn.
Applicant's arguments filed 03/04/2026regarding the rejection under 35 USC 101 have been fully considered and are not persuasive.
Applicant’s arguments with respect to claim(s) 1 and 11 have been considered but are moot because the new ground of rejection.
The rejection under 35 USC 103 is maintained.
Claim Rejections - 35 USC § 112
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, 10-11, and 20 are 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.
Regarding claims 1, and 11 recites the limitation “based on the air conditioner being identified as only capable of cooling” an “the heating history information includes data on operation factors corresponding to a heating operation of the air conditioner”. This is indefinite because it is not clear how the heating history information corresponds to a heating operation of an air conditioner that is not capable of heating/ only capable of cooling. If the air conditioner is only capable of cooling it would not have any heating operations and thus would not have any heating operation information associated with it.
Regarding claims 10, and 20 recites the limitation “a model that has been trained based on cooling operation history information and heating operation history information of the air conditioner leaking refrigerant, and cooling operation history information and heating operation history information of the air conditioner not leaking refrigerant.” This is indefinite because it is not clear how a model is trained on heating information from an air conditioner that is only capable of cooling therefore not producing heating operation information.
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.
Claims 1-2, 5-6, 9-12, 15-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. Pub. No.: US 20200208861 A1 in view of Moon Pub No.: KR20230022546A and Sasaki et al. Pub. No.: WO 2017056403 A1.
Regarding claim 1 Chen teaches An electronic apparatus comprising: a communication interface; a memory configured to store a first artificial intelligence model for predicting whether refrigerant leaks from an air conditioner, …1 and at least one instruction; and a processor disposed in electrical communication with the memory, the processor being configured to control the electronic apparatus, wherein the processor is configured to, (Chen, paragraph 0098-0101, teaches the use of a processor that is attached to a memory that is able to store a neural network and its instructions (i.e. artificial intelligence). Chen, paragraph 0086, teaches the use of a communication interface in the operation of the air-conditioning unit.)
by executing the at least one instruction, receive, through the communication interface from the air conditioner, first operation history information of the air conditioner and device information of the air conditioner, 2… based on the air conditioner being identified as only capable of cooling, 3…, (Chen, paragraph 0058-0061, teaches the collecting and use of operating parameters (i.e. operation history information) and device information for the use in the leak prediction neural network)
predict whether the refrigerant leaks from the air conditioner by inputting either the first operation history information or the second operation history information into the first artificial intelligence model (Chen, paragraph 0085-0088, teaches the prediction of weather refrigerant will leak using a neural network based off of an input operation history information.)
provide prediction information on the refrigerant leakage based on a result of the prediction of whether the refrigerant leaks from the air conditioner (Chen, paragraph 0038-0040, teaches the outputting of prediction information based on whether the refrigerant will leak or not.) And control the air conditioner based on the result of the prediction of whether the refrigerant leaks from the air conditioner. (Chen, paragraph 0041, teaches the controlling of an air conditioner system based on if a refrigerant leak is detected or not.)
Chen does not teach …1, a second artificial intelligence model for converting cooling history information into heating history information by learning a correlation between the cooling history information and the heating history information of the air conditioner, … However, Moon in analogous art teaches this limitation (Moon, paragraph 0032-0038, teaches the training of a learning unit that is a machine learning model that is trained on heating and cooling data and learns a correlation between the two data.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Moon’s teaching of using heating and cooling data from an air conditioning system with the combination of Chen teaching of refrigerant leakage prediction. The motivation to do so would be to allow the system to more accurately predict leaking in scenarios where either heating or cooling was actively being used at the time of the leak.
However, the combination of Chen and Moon do not teach 2…identify a temperature using a sensor and air-conditioning capability of the air conditioner, based on the temperature being lower than or equal to a threshold value and … However, Sasaki in analogous art teaches this limitation (Sasaki, page 17, paragraphs, 4-5, teaches the use of a temperature sensor to get the temperature and compare it to a threshold in order to determine if an air conditioner should be on heating, cooling or off.)
Further, the combination of Chen and Moon do not teach 3…acquire second operation history information by inputting the first operation history information into the second artificial intelligence model… However, Sasaki in analogous art teaches this limitation (Sasaki, page 7 – paragraph 7, page 8 – paragraph 6, page 10 – paragraph 5, teaches the ability of a machine learning model to be able to predict the operating information that is needed to achieve a predicted temperature when changing from heating to cooling or from an on to an off state.)
Further, the combination of Chen and Moon do not teach wherein the cooling history information includes data on operation factors corresponding to a cooling operation of the air conditioner, and the heating history information includes data on operation factors corresponding to a heating operation of the air conditioner. However, Sasaki in analogous art teaches this limitation (Sasaki, page 7 – paragraph 7, page 8 – paragraph 6, page 10 – paragraph 5, teaches the use of operational information and history information that relates to the heating and cooling operations of an air conditioning unit.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Sasaki’s teaching of using a sensor to detect the current temperature and cause the air conditioner to do something if that temperature is below a threshold with the combination of Chen and Moon’s teaching of refrigerant leakage prediction. The motivation to do so would be to allow the air conditioning system to utilize more operational data of the air conditioner and create data when there is an insufficient amount in order to train the leak prediction algorithm to better predict leaks by maximizing the amount of data available.
Regarding Claim 2 the combination of Chen, Moon, and Sasaki teaches wherein the processor is further configured to, based on the first operation history information being converted to the second operation history information, (Sasaki, page 7 – paragraph 7, page 8 – paragraph 6, page 10 – paragraph 5, teaches the ability of a machine learning model to be able to predict the operating information that is needed to achieve a predicted temperature when changing from heating to cooling or from an on to an off state.)predict whether the refrigerant leaks from the air conditioner by inputting the second operation history information into the first artificial intelligence model. (Chen, paragraph 0085-0088, teaches the predicting of weather refrigerant will leak using a neural network based off of an inputted operation history information.)
Regarding Claim 5 the combination of Chen, Moon, and Sasaki teaches The apparatus of claim 1, wherein the first operation history information is configured to be cooling history information of the air conditioner, and the second operation history information is configured to be heating history information of the air conditioner. (Sasaki, page 7 – paragraph 7, page 8 – paragraph 6, page 10 – paragraph 5, teaches the use of operational information and history information that relates to the heating and cooling operations of an air conditioning unit.)
Regarding Claim 6 the combination of Chen, Moon, and Sasaki teaches The apparatus of claim 1, wherein the processor is configured to, based on the air conditioner being identified as capable of cooling and heating However, Moon in analogous art teaches this limitation (Moon, paragraph 0032-0038, teaches the use of an air conditioning system that is capable of both heating and cooling.) predict whether the refrigerant leaks from the air conditioner by inputting the first operation history information into the first artificial intelligence model . (Chen, paragraph 0085-0088, teaches the prediction of weather refrigerant will leak using a neural network based off of an inputted operation history information.)
Regarding Claim 9 the combination of Chen, Moon, and Sasaki teaches The apparatus of claim 1, wherein the first operation history information includes the temperature information of the region in proximity to the air conditioner, and the device information includes information on whether the air conditioner is capable of cooling or heating. (Chen, paragraph 0058-0061, teaches the gathering and use of environmental data (i.e. region in proximity) that the air conditioning unit is in and includes the operational information about whether or not the air conditioner is able to cool and heat.)
Regarding Claim 10 the combination of Chen, Moon, and Sasaki teaches The apparatus of claim 1, wherein the first artificial intelligence model is configured to be a model that has been trained based on cooling operation history information and heating operation history(Moon, paragraph 0032-0038, teaches the training of a learning unit that is a machine learning model that is trained on heating and cooling data and learns a correlation between the two data.) information of the air conditioner leaking refrigerant, and cooling operation history information and heating operation history information of the air conditioner not leaking refrigerant. . (Chen, paragraph 0058-0064, teaches the selecting of data including leaking and non-leaking training data and cooling data and teaches the training of the machine learning model using said selected data.)
Regarding claim 11 Chen teaches A method for controlling an electronic apparatus comprising: 1… receiving, from an the air conditioner, first operation history information of the air conditioner and device information of the air conditioner; 2… (Chen, paragraph 0058-0061, teaches the collecting and use of operating parameters (i.e. operation history information) and device information for the use in the leak prediction neural network)
predicting whether the refrigerant leaks from the air conditioner by inputting either the first operation history information or the second operation history information into the first artificial intelligence model 2… and based on the air conditioner being identified as only capable of cooling3… (Chen, paragraph 0085-0088, teaches the predicting of weather refrigerant will leak using a neural network based off of an inputted operation history information.)
predicting whether refrigerant leaks from the air conditioner by inputting either the first operation history information or the second operation history information into a-the first artificial intelligence model; (Chen, paragraph 0085-0088, teaches the prediction of weather refrigerant will leak using a neural network based off of an inputted operation history information.)
and providing prediction information on the refrigerant leakage based on a result of the prediction of whether the refrigerant leaks from the air conditioner. (Chen, paragraph 0038-0040, teaches the outputting of prediction information based on whether the refrigerant will leak or not.) and controlling the air conditioner based on the result of the prediction of whether the refrigerant leaks from the air conditioner. (Chen, paragraph 0041, teaches the controlling of an air conditioner system based on if a refrigerant leak is detected or not.)
However, Chen does not teach 1… and a second artificial intelligence model for converting cooling history information into heating history information by learning a correlation between the cooling history information and the heating history information of the air conditioner… However, Moon in analogous art teaches this limitation (Moon, paragraph 0032-0038, teaches the training of a learning unit that is a machine learning model that is trained on heating and cooling data and learns a correlation between the two data.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Moon’s teaching of using heating and cooling data from an air conditioning system with the combination of Chen teaching of refrigerant leakage prediction. The motivation to do so would be to allow the system to more accurately predict leaking in scenarios where either heating or cooling was actively being used at the time of the leak.
However, the combination of Chen and Moon do not teach 2…identifying a temperature using a sensor and air-conditioning capability of the air conditioner, based on the temperature around the air conditioner being lower than or equal to a threshold value However, Sasaki in analogous art teaches this limitation (Sasaki, page 17, paragraphs, 4-5, teaches the use of a temperature sensor to get the temperature and compare it to a threshold in order to determine if an air conditioner should be on heating, cooling or off.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Sasaki’s teaching of using a sensor to detect the current temperature and cause the air conditioner to do something if that tempered is below a threshold with the combination of Chen and Moon’s teaching of refrigerant leakage prediction. The motivation to do so would be to allow the air conditioning system to detect the current temperature in order to see if the temperature is being affected by the air conditioner to allow the system to detect if it is functioning properly.
However, the combination of Chen and Moon do not teach 2…identify a temperature using a sensor and air-conditioning capability of the air conditioner, based on the temperature being lower than or equal to a threshold value and … However, Sasaki in analogous art teaches this limitation (Sasaki, page 17, paragraphs, 4-5, teaches the use of a temperature sensor to get the temperature and compare it to a threshold in order to determine if an air conditioner should be on heating, cooling or off.)
Further, the combination of Chen and Moon do not teach 3…acquire second operation history information by inputting the first operation history information into the second artificial intelligence model… However, Sasaki in analogous art teaches this limitation (Sasaki, page 7 – paragraph 7, page 8 – paragraph 6, page 10 – paragraph 5, teaches the ability of a machine learning model to be able to predict the operating information that is needed to achieve a predicted temperature when changing from heating to cooling or from an on to an off state.)
Further, the combination of Chen and Moon do not teach wherein the cooling history information includes data on operation factors corresponding to a cooling operation of the air conditioner, and the heating history information includes data on operation factors corresponding to a heating operation of the air conditioner. However, Sasaki in analogous art teaches this limitation (Sasaki, page 7 – paragraph 7, page 8 – paragraph 6, page 10 – paragraph 5, teaches the use of operational information and history information that relates to the heating and cooling operations of an air conditioning unit.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Sasaki’s teaching of using a sensor to detect the current temperature and cause the air conditioner to do something if that temperature is below a threshold with the combination of Chen and Moon’s teaching of refrigerant leakage prediction. The motivation to do so would be to allow the air conditioning system to utilize more operational data of the air conditioner and create data when there is an insufficient amount in order to train the leak prediction algorithm to better predict leaks by maximizing the amount of data available.
Regarding Claim 12 the combination of Chen, Moon, and Sasaki teaches The method of claim 11, wherein the predicting whether the refrigerant leaks includes,, based on the first operation history information being converted to the second operation history information, (Sasaki, page 7 – paragraph 7, page 8 – paragraph 6, page 10 – paragraph 5, teaches the ability of a machine learning model to be able to predict the operating information that is needed to achieve a predicted temperature when changing from heating to cooling or from an on to an off state.) predict whether the refrigerant leaks from the air conditioner by inputting the second operation history information into the first artificial intelligence model. (Chen, paragraph 0085-0088, teaches the predicting of weather refrigerant will leak using a neural network based off of an inputted operation history information.)
Regarding Claim 15 the combination of Chen, Moon, and Sasaki teaches The method of claim 11, wherein the first operation history information is configured to be cooling history information of the air conditioner, and the second operation history information is configured to be heating history information of the air conditioner. (Sasaki, page 7 – paragraph 7, page 8 – paragraph 6, page 10 – paragraph 5, teaches the use of operational information and history information that relates to the heating and cooling operations of an air conditioning unit.)
Regarding Claim 16 the combination of Chen, Moon, and Sasaki teaches The method of claim 11, wherein the processor is configured to, based on the air conditioner being identified as capable of cooling and heating However, Moon in analogous art teaches this limitation (Moon, paragraph 0032-0038, teaches the use of an air conditioning system that is capable of both heating and cooling.) predict whether the refrigerant leaks from the air conditioner by inputting the first operation history information into the first artificial intelligence model . (Chen, paragraph 0085-0088, teaches the prediction of weather refrigerant will leak using a neural network based off of an inputted operation history information.)Regarding Claim 19 the combination of Chen, Moon, and Sasaki teaches the method of claim 11, wherein the first operation history information includes the temperature information of the region in proximity to the air conditioner, and the device information includes information on whether the air conditioner is capable of cooling or heating. (Chen, paragraph 0058-0061, teaches the gathering and use of environment data (i.e. region in proximity) that the air conditioning unit is in and includes the operational information about whether or not the air conditioner is able to cool and heat.)
Regarding Claim 20 the combination of Chen, Moon, and Sasaki teaches The method of claim 11, wherein the first artificial intelligence model is configured to be a model that has been trained based on cooling operation history information and heating operation history(Moon, paragraph 0032-0038, teaches the training of a learning unit that is a machine learning model that is trained on heating and cooling data and learns a correlation between the two data.) information of the air conditioner leaking refrigerant, and cooling operation history information and heating operation history information of the air conditioner not leaking refrigerant. . (Chen, paragraph 0058-0064, teaches the selecting of data including leaking and non-leaking training data and cooling data and teaches the training of the machine learning model using said selected data.)
Claims 7-8, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. Pub. No.: US 20200208861 A1 in view of Moon Pub No.: KR20230022546A and Sasaki et al. Pub. No.: WO 2017056403 A1in further view of Fukazawa Pub. No.: JP2009014233A.
Regarding Claim 7 the combination of Chen, Moon, and Sasaki teaches The apparatus of claim 6, wherein the processor is configured to, based on temperature of the region in proximity to the air conditioner (Chen, paragraph 0058-0061, teaches the gathering and use of environment data (i.e. region in proximity) that the air conditioning unit is in) 5…and the first operation history information being cooling history information, convert the first operation history information into the second operation history information. , (Sasaki, page 7 – paragraph 7, page 8 – paragraph 6, page 10 – paragraph 5, teaches the ability of a machine learning model to be able to predict the operating information that is needed to achieve a predicted temperature when changing from heating to cooling or from an on to an off state.)
the combination of Chen, Moon, and Sasaki does not teach 5…being lower than or equal to a threshold value… However, Fukazawa in analogous art teaches this limitation (Fukazawa, paragraph 0040-0041, teaches the use of a predetermined threshold based on air conditioner data to determine if an action needs to be taken.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Fukazawa’s teaching of using thresholds with air conditioning data with the combination of Chen, Moon, and Sasaki’s teaching of refrigerant leakage prediction. The motivation to do so would be to be able to determine if the environment that the air conditioning is placed in is in need of cooling or heating and collect data based on the state of the environment.
Regarding Claim 8 the combination of Chen, Moon, and Sasaki teaches teaches The apparatus of claim 11, …predict whether the refrigerant leaks from the air conditioner by inputting the first operation history information into the first artificial intelligence model. (Chen, paragraph 0085-0088, teaches the predicting of weather refrigerant will leak using a neural network based off of an inputted operation history information.)
The combination of Chen, Moon, and Sasaki does not teach… wherein the processor is configured to, based on temperature of the region in proximity to the air conditioner exceeding a threshold value However, Fukazawa in analogous art teaches this limitation (Fukazawa, paragraph 0040-0041, teaches the use of a predetermined threshold based on air conditioner data to determine if an action needs to be taken.),
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Fukazawa’s teaching of using thresholds with air conditioning data with the combination of Chen, Moon, and Sasaki’s teaching of refrigerant leakage prediction. The motivation to do so would be to be able to determine if the environment that the air conditioning is placed in is in need of cooling or heating and collect data based on the state of the environment.
Regarding Claim 17 the combination of Chen, Moon, and Sasaki teaches The method of claim 16, wherein the processor is configured to, based on temperature of the region in proximity to the air conditioner (Chen, paragraph 0058-0061, teaches the gathering and use of environment data (i.e. region in proximity) that the air conditioning unit is in) 5…and the first operation history information being cooling history information, convert the first operation history information into the second operation history information. , (Sasaki, page 7 – paragraph 7, page 8 – paragraph 6, page 10 – paragraph 5, teaches the ability of a machine learning model to be able to predict the operating information that is needed to achieve a predicted temperature when changing from heating to cooling or from an on to an off state.)
the combination of Chen, Moon, and Sasaki not teach 5…being lower than or equal to a threshold value… However, Fukazawa in analogous art teaches this limitation (Fukazawa, paragraph 0040-0041, teaches the use of a predetermined threshold based on air conditioner data to determine if an action needs to be taken.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Fukazawa’s teaching of using thresholds with air conditioning data with the combination of Chen, Moon, and Sasaki’s teaching of refrigerant leakage prediction. The motivation to do so would be to be able to determine if the environment that the air conditioning is placed in needs cooling or heating and collect data based on the state of the environment.
Regarding Claim 18 the combination of Chen, Moon, and Sasaki teaches The method of claim 11…, predict whether the refrigerant leaks from the air conditioner by inputting the first operation history information into the first artificial intelligence model. (Chen, paragraph 0085-0088, teaches the predicting of weather refrigerant will leak using a neural network based off of an inputted operation history information.)
The combination of Chen, Moon, and Sasaki does not teach …wherein the processor is configured to, based on temperature of the region in proximity to the air conditioner exceeding a threshold value… However, Fukazawa in analogous art teaches this limitation (Fukazawa, paragraph 0040-0041, teaches the use of a predetermined threshold based on air conditioner data to determine if an action needs to be taken.),
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Fukazawa’s teaching of using thresholds with air conditioning data with the combination of Chen, Moon, and Sasaki’s teaching of refrigerant leakage prediction. The motivation to do so would be to be able to determine if the environment that the air conditioning is placed in is in need of cooling or heating and collect data based on the state of the environment.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 7am-5pm; F: Out of Office.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MARIELA REYES can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS BERNARD LANE/Examiner, Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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