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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over FOR1 (EP3904777A1 – provided by Applicant in the IDS) in view of FOR2 (CN114462467A).
Regarding claim 1, FOR1 teaches a prediction apparatus (600, 700, Figs. 6-7) for outputting information for controlling a start timing of a defrosting operation (see paragraph [0098], “The server 700 may include a defrost control device 600 which will be described later with reference to FIG. 6 and the like. Alternatively, the server 700 may include at least defrost controller 670”), in a case where the defrosting operation is started on a heat exchanger functioning as a heat absorber (see paragraph [0098], “Accordingly, the server 700 may determine whether the outdoor heat exchanger is frosted based on the image data of the outdoor heat exchanger photographed by the camera module 355, and may predict the timing of frosting based on the sensing data detected by the sensing unit 350”), the defrosting operation being performed by a refrigerator including a refrigerant circuit (see Fig. 2) in which a refrigerant circulates, the refrigerant circuit including a compressor (102, Fig. 2, paragraph [0066]), a radiator (108, Fig. 2, paragraph [0066]), an expansion valve (106, Fig. 2, paragraph [0066]), and the heat absorber that are annularly connected (104, Fig. 2, paragraph [0066]), the prediction apparatus comprising:
a processor (600, Fig. 6) configured to
input operation data of a current time in the refrigerator into a learned model (see paragraph [0111], “Meanwhile, the defrost controller 670 may include a frosting detection unit 671 that classifies image data of the outdoor heat exchanger 104 into image data of frosting and normal states by using a Convolutional Neural Network (CNN)-based image classification model, and a frosting start timing prediction unit 672 that predicts the remaining time until frosting by using one or more machine learning models when the cycle operation data of the air conditioner 50 is input”),
wherein the learned model is generated based on learning data obtained by classifying a history of past operation data of the refrigerator into a plurality of groups, assigning, to each of the plurality of groups, data indicating presence or absence of frost formation on the heat exchanger (see paragraph [0109], “Meanwhile, the defrost control device 600 may receive image data and sensing data of the outdoor heat exchanger through the communication unit 660, and may determine, through the defrost controller 670, whether the outdoor heat exchanger 104 is in a frosting state or a normal state based on the received data, and predict the time when frosting occurs”), and
output information indicating presence or absence of frost formation on the heat exchanger predicted by the learned model (see paragraph [0113], “When receiving the cycle operation data obtained from the sensing unit 350 in real time, the frosting start timing prediction unit 672 predicts a corresponding frosting timing and predicts the remaining time until frosting by using one or more machine learning models of Random Forest, Deep Neural Network (DNN), and CNN”).
FOR1 does not specifically teach classifying a history of past operation data of the refrigerator into a plurality of groups by clustering. FOR2 teaches a recommendation method which comprises a clustering characteristic data of a user and using a clustering algorithm and generating user clustering centres according t the history of data (FOR2, Abstract).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide FOR1 with classifying past data into a plurality of groups by clustering, as taught by FOR2, in order to assess how the use clustering impacts the desired optimization of the prediction apparatus.
Regarding claim 2, FOR1 teaches the prediction apparatus according to claim 1, wherein the operation data includes data indicating a state of the refrigerant circuit (see at least paragraph [0109], “determine, through the defrost controller 670, whether the outdoor heat exchanger 104 is in a frosting state or a normal state based on the received data, and predict the time when frosting occurs”).
Regarding claim 3, FOR1 teaches the prediction apparatus according to claim 2, wherein the operation data includes data indicating a state of an environment (see at least paragraph [0109], “determine, through the defrost controller 670, whether the outdoor heat exchanger 104 is in a frosting state or a normal state based on the received data, and predict the time when frosting occurs” the Examiner notes the state of frosting on the heat exchanger is the indication of a state of the environment of the heat exchanger).
Regarding claim 4, FOR1 teaches the prediction apparatus according to claim 3, wherein the data indicating the state of the refrigerant circuit includes:
any one of:
the data indicating the state of the environment includes environmental humidity data (see paragraph [0127], “data receiving unit is a device that collects image data in frosting and normal states collected from the camera module 355 attached to the heat exchanger 104 of the outdoor unit 30, and operation data received from the temperature sensor, the humidity sensor, and the pressure sensor installed throughout the air conditioner 50 such as evaporator, condenser, and compressor 102”);
a frequency of the compressor, pressure data on a low pressure side of the compressor, opening data of the expansion valve, heating capacity data, and evaporation temperature data (the limitations are not required as the claim is claimed in the alternative).
Regarding claim 5, FOR1 teaches the prediction apparatus according to claim 1, wherein the processor is configured to output the information indicating the start timing of the defrosting operation as the information indicating presence or absence of frost formation on the heat exchanger when frost formation present is predicted by the learned model (see at least paragraph [0109], “determine, through the defrost controller 670, whether the outdoor heat exchanger 104 is in a frosting state or a normal state based on the received data, and predict the time when frosting occurs”).
Regarding claim 6, FOR1 teaches the prediction apparatus according to claim 1, wherein the processor is configured to output generate the learning data by classifying the history of the past operation data of the refrigerator into the plurality of groups by clustering and assigning, to each of the plurality of groups, the data indicating the presence or absence of frost formation on the heat exchanger (see paragraph [0110], “To this end, the defrost controller 670 may use a machine learning model that performs two different roles. It is possible to determine whether the air conditioner is frosted by using a machine learning model that discerns the current frosting of air conditioner, and it is possible to predict the next frosting occurrence time by using a machine learning model that predict the time when frosting will occur, thereby minimizing the defrost operation time and preventing the degradation of heating performance caused by the defrost operation”).
Regarding claim 7, FOR1 teaches the prediction apparatus according to claim 1, wherein the processor is configured to output input, into a learning model (see paragraph [0127]), the history of the past operation data classified into the plurality of groups of the learning data, thereby learning the learning model so that output data output from the learning model approaches the data that is assigned to each of the plurality of groups and that indicates presence or absence of frost formation on the heat exchanger, thereby generating the learned model (see paragraph [0110], “To this end, the defrost controller 670 may use a machine learning model that performs two different roles. It is possible to determine whether the air conditioner is frosted by using a machine learning model that discerns the current frosting of air conditioner, and it is possible to predict the next frosting occurrence time by using a machine learning model that predict the time when frosting will occur, thereby minimizing the defrost operation time and preventing the degradation of heating performance caused by the defrost operation” further see paragraphs [0111] and [0113]).
Regarding claim 8, FOR1 teaches a refrigeration system comprising: the refrigerator that controls the start timing of the defrosting operation by using the information indicating presence or absence of frost formation on the heat exchanger output by the prediction apparatus according to claim 1 (see Fig. 2, claim 1).
Regarding claim 9, FOR1 teaches the refrigeration system according to claim 8, wherein the prediction apparatus is implemented in any one of a plurality of units included in the refrigerator (the model can be implemented on either heat exchanger in Fig. 2).
Regarding claim 10, FOR1 teaches the refrigeration system according to claim 8, further comprising: a server apparatus connected to the refrigerator via a network, wherein the prediction apparatus is implemented in the server apparatus (see Fig. 4).
Regarding claim 11, FOR1 teaches the prediction method of a prediction apparatus (600, 700, Figs. 6-7) for outputting information for controlling a start timing of a defrosting operation (see paragraph [0098], “The server 700 may include a defrost control device 600 which will be described later with reference to FIG. 6 and the like. Alternatively, the server 700 may include at least defrost controller 670”), in a case where the defrosting operation is started on a heat exchanger functioning as a heat absorber (see paragraph [0098], “Accordingly, the server 700 may determine whether the outdoor heat exchanger is frosted based on the image data of the outdoor heat exchanger photographed by the camera module 355, and may predict the timing of frosting based on the sensing data detected by the sensing unit 350”), the defrosting operation being performed by a refrigerator including a refrigerant circuit in which a refrigerant circulates (see Fig. 2), the refrigerant circuit including a compressor (102, Fig. 2, paragraph [0066]), a radiator (108, Fig. 2, paragraph [0066]), an expansion valve (106, Fig. 2, paragraph [0066]), and the heat absorber that are annularly connected (104, Fig. 2, paragraph [0066]), the prediction method comprising:
inputting operation data of a current time in the refrigerator into a learned model (see paragraph [0111], “Meanwhile, the defrost controller 670 may include a frosting detection unit 671 that classifies image data of the outdoor heat exchanger 104 into image data of frosting and normal states by using a Convolutional Neural Network (CNN)-based image classification model, and a frosting start timing prediction unit 672 that predicts the remaining time until frosting by using one or more machine learning models when the cycle operation data of the air conditioner 50 is input”),
the learned model is generated based on learning data obtained by classifying a history of past operation data of the refrigerator into a plurality of groups and assigning, to each of the plurality of groups, data indicating presence or absence of frost formation on the heat exchanger (see paragraph [0109], “Meanwhile, the defrost control device 600 may receive image data and sensing data of the outdoor heat exchanger through the communication unit 660, and may determine, through the defrost controller 670, whether the outdoor heat exchanger 104 is in a frosting state or a normal state based on the received data, and predict the time when frosting occurs”); and
outputting information indicating presence or absence of frost formation on the heat exchanger predicted by the learned model (see paragraph [0113], “When receiving the cycle operation data obtained from the sensing unit 350 in real time, the frosting start timing prediction unit 672 predicts a corresponding frosting timing and predicts the remaining time until frosting by using one or more machine learning models of Random Forest, Deep Neural Network (DNN), and CNN”).
FOR1 does not specifically teach classifying a history of past operation data of the refrigerator into a plurality of groups by clustering. FOR2 teaches a recommendation method which comprises a clustering characteristic data of a user and using a clustering algorithm and generating user clustering centres according t the history of data (FOR2, Abstract).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide FOR1 with classifying past data into a plurality of groups by clustering, as taught by FOR2, in order to assess how the use clustering impacts the desired optimization of the prediction apparatus.
Regarding claim 12, FOR1 teaches a non-transitory computer-readable recording medium storing a prediction program that causes a computer to execute a process performed in a prediction apparatus (600, 700, Figs. 6-7), the prediction apparatus being for outputting information for controlling a start timing of a defrosting operation (see paragraph [0098], “The server 700 may include a defrost control device 600 which will be described later with reference to FIG. 6 and the like. Alternatively, the server 700 may include at least defrost controller 670”), in a case where the defrosting operation is started on a heat exchanger functioning as a heat absorber (see paragraph [0098], “Accordingly, the server 700 may determine whether the outdoor heat exchanger is frosted based on the image data of the outdoor heat exchanger photographed by the camera module 355, and may predict the timing of frosting based on the sensing data detected by the sensing unit 350”), the defrosting operation being performed by a refrigerator including a refrigerant circuit in which a refrigerant circulates (see Fig. 2), the refrigerant circuit including a compressor (102, Fig. 2, paragraph [0066]), a radiator (108, Fig. 2, paragraph [0066]), an expansion valve (106, Fig. 2, paragraph [0066]), and the heat absorber that are annularly connected (104, Fig. 2, paragraph [0066]), the process comprising:
inputting operation data of a current time in the refrigerator into a learned model (see paragraph [0111], “Meanwhile, the defrost controller 670 may include a frosting detection unit 671 that classifies image data of the outdoor heat exchanger 104 into image data of frosting and normal states by using a Convolutional Neural Network (CNN)-based image classification model, and a frosting start timing prediction unit 672 that predicts the remaining time until frosting by using one or more machine learning models when the cycle operation data of the air conditioner 50 is input”),
in a case where the learned model is generated based on learning data that is obtained by classifying a history of past operation data of the refrigerator into a plurality of groups by assigning, to each of the plurality of groups, data indicating presence or absence of frost formation on the heat exchanger (see paragraph [0109], “Meanwhile, the defrost control device 600 may receive image data and sensing data of the outdoor heat exchanger through the communication unit 660, and may determine, through the defrost controller 670, whether the outdoor heat exchanger 104 is in a frosting state or a normal state based on the received data, and predict the time when frosting occurs”); and
outputting information indicating presence or absence of frost formation on the heat exchanger predicted by the learned model (see paragraph [0113], “When receiving the cycle operation data obtained from the sensing unit 350 in real time, the frosting start timing prediction unit 672 predicts a corresponding frosting timing and predicts the remaining time until frosting by using one or more machine learning models of Random Forest, Deep Neural Network (DNN), and CNN”).
FOR1 does not specifically teach classifying a history of past operation data of the refrigerator into a plurality of groups by clustering. FOR2 teaches a recommendation method which comprises a clustering characteristic data of a user and using a clustering algorithm and generating user clustering centres according t the history of data (FOR2, Abstract).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide FOR1 with classifying past data into a plurality of groups by clustering, as taught by FOR2, in order to assess how the use clustering impacts the desired optimization of the prediction apparatus.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 11, 12 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 NAEL N BABAA whose telephone number is (571)270-3272. The examiner can normally be reached M-F, 9-5 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry-Daryl Fletcher can be reached at (571)-270-5054. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAEL N BABAA/Primary Examiner, Art Unit 3763
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