Office Action 2: Final Rejection (2025-05-29)
Rejection types: 103 - Obviousness

CLAIMS:
LISTING OF CLAIMS

In accordance with 37 C.F.R. § 1.121, a claim listing including the status and text of all claims as currently presented appears below.

1\. (Currently Amended) A washing machine appliance, comprising:
a wash tub positioned within a cabinet;
a wash basket rotatably mounted within the wash tub and defining a wash chamber configured for receiving a load of clothes;
a water supply configured to selectively dispense a flow of wash fluid through a discharge nozzle into the wash tub;
a bulk dispenser for selectively adding a wash additive to the flow of wash fluid;
a camera assembly mounted within the cabinet above the wash chamber and faces the discharge nozzle to monitor the flow of wash fluid; and
a controller operably coupled to the water supply, the bulk dispenser, and \[\[a\]\] the camera assembly, the controller being configured to:initiate a bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive;
obtain one or more images of the flow of wash fluid using the camera assembly before the wash fluid reaches the load of clothes;
analyze the one or more images using an image recognition process to determine an additive content of the flow of wash fluid;
determine that the additive content falls below a predetermined additive threshold;
and terminate the bulk flush operation in response to determining that the additive content falls below the predetermined additive threshold.
2\. (Original) The washing machine appliance of claim 1, wherein the controller is further configured to:
determine that the additive content exceeds the predetermined additive threshold; and

continue the bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive.
3\. (Original) The washing machine appliance of claim 1, wherein the controller is further configured to:
analyze the one or more images using the image recognition process to determine that a flow rate of the flow of wash fluid falls below a flow rate threshold; and
provide a user notification that there is a fault condition with the water supply.
4\. (Original) The washing machine appliance of claim 1, wherein terminating the bulk flush operation comprises stopping the flow of wash fluid using the water supply and stopping the dispensing of the wash additive using the bulk dispenser.
5\. (Original) The washing machine appliance of claim 1, wherein terminating the bulk flush operation comprises providing a user notification that a bulk dispensing tank is empty.
6\. (Original) The washing machine appliance of claim 5, further comprising:
a user interface panel, wherein the user notification is provided through the user interface panel.
7\. (Original) The washing machine appliance of claim 5, wherein the controller is in operative communication with a remote device through an external network, and wherein the user notification is provided through the remote device.
8\. (Original) The washing machine appliance of claim 1, wherein the image recognition process is a machine learning image recognition process comprising at least one of a convolution neural network ("CNN"), a region-based convolution neural network ("R-CNN"), a deep belief network ("DBN"), or a deep neural network ("DNN") image recognition process.
9\. (Canceled)

10\. (Canceled)

11\. (Original) The washing machine appliance of claim 1, wherein the controller is in operative communication with a remote device through an external network, wherein the remote device comprises the camera assembly.

12\. (Original) The washing machine appliance of claim 1, wherein the washing machine appliance is a vertical axis washing machine appliance.

13\. (Currently Amended) A method of operating a washing machine appliance, the washing machine appliance comprising a wash basket rotatably mounted within a wash tub and defining a wash chamber configured for receiving a load of clothes, a water supply configured to selectively dispense a flow of wash fluid through a discharge nozzle into the wash tub, and a bulk dispenser for selectively adding a wash additive to the flow of wash fluid, the method comprising:
initiating a bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive;
obtaining one or more images of the flow of wash fluid using a camera assemblytie camera assembly being mounted to face the discharge nozzle to monitor the flow of wash fluid before the wash fluid reaches the load of clothes;
analyzing the one or more images using an image recognition process to determine an additive content of the flow of wash fluid;
determining that the additive content falls below a predetermined additive threshold; and terminating the bulk flush operation in response to determining that the additive content falls below the predetermined additive threshold.

14\. (Original) The method of claim 13, further comprising:
determining that the additive content exceeds the predetermined additive threshold; and continuing the bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive.

15\. (Original) The method of claim 13, further comprising:
analyzing the one or more images using the image recognition process to determine that a flow rate of the flow of wash fluid falls below a flow rate threshold; and
providing a user notification that there is a fault condition with the water supply.

16\. (Original) The method of claim 13, wherein terminating the bulk flush operation comprises stopping the flow of wash fluid using the water supply and stopping the dispensing of the wash additive using the bulk dispenser.

17\. (Original) The method of claim 13, wherein terminating the bulk flush operation comprises providing a user notification that a bulk dispensing tank is empty.

18\. (Original) The method of claim 13, wherein the image recognition process is a machine learning image recognition process comprising at least one of a convolution neural network ("CNN"), a region-based convolution neural network ("R-CNN"), a deep belief network ("DBN"), or a deep neural network ("DNN") image recognition process.

19\. (Canceled)

20\. (Original) The method of claim 13, wherein the washing machine appliance is in operative communication with a remote device through an external network, wherein the remote device comprises the camera assembly.

OFFICE ACTION:
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 .
Response to Arguments
Applicant’s arguments, see pages 6-8, filed March 18, 2024, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 103 have been fully considered and are persuasive.  Therefore, the rejection has been withdrawn.  However, upon further consideration, a new ground(s) of rejection is made in view of Yoon (US 2023/0067550).
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.
Claim(s) 1-2, 4-8, 11-14, 16-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 2020/0071871), and in further view of Yoon (US 2023/0067550) and Gallagher (US 2017/0037559).
Regarding claim 1, Kim figures 1-4 teach a washing machine appliance, comprising:
a wash tub (8 tub) positioned within a cabinet (2);
a wash basket (10 drum) rotatably mounted within the wash tub and defining a wash chamber configured for receiving a load of clothes;
a water supply (30 water supply part) configured to selectively dispense a flow of wash fluid into the wash tub;
a bulk dispenser (60 detergent supply part) for selectively adding a wash additive to the flow of wash fluid;
a controller (360) operably coupled to the water supply, the bulk dispenser, and a camera assembly (322 image sensor).[0037-41][0054-57]
Kim teaches a detergent bubble recognizer 110 for recognizing presence of detergent bubble from image data. The detergent bubble recognizer 110 may recognize presence of detergent bubble from the image data acquired by the image sensor 322 using an image recognition model based on machine learning as a field of artificial intelligence.  As an example of artificial intelligence technology applied to the detergent bubble recognizer 110 of the additional rinsing control module 100. The additional rinsing control module 100 may include a bubble ratio calculator 130 for calculating the ratio of detergent bubble based on the detergent bubble region extracted from the image data. The additional rinsing control module 100 may include an additional rinsing setting unit 140 for setting an additional rinsing cycle based on the ratio of detergent bubble calculated by the bubble ratio calculator 130. The additional rinsing setting unit 140 may set the number of times of performing the additional rinsing cycle and/or a time when the additional rinsing cycle is performed based on the calculated ratio of detergent bubble. The washing machine 1 according to the embodiment of the present invention may recognize presence of residual detergent (detergent bubble) from image data using an image recognition model based on artificial intelligence and perform control to perform the additional rinsing cycle based on the result of recognition, thereby more efficiently performing the rinsing cycle of the washing operation thereby reading on the controller being configured to obtain one or more images of the flow of wash fluid using the camera assembly, analyze the one or more images using an image recognition process to determine an additive content of the flow of wash fluid; determine that the additive content falls below a predetermined additive threshold; and terminate the bulk flush operation in response to determining that the additive content falls below the predetermined additive threshold.[0074, 0077, 0110, 0112-113, 0116]
Kim is silent to a discharge nozzle, a camera assembly mounted within the cabinet above the wash chamber and faces the discharge nozzle to monitor the flow of wash fluid, and the controller configured to obtain one or more images of the flow of wash fluid using the camera assembly before the wash fluid reaches the load of clothes.
Yoon is directed towards a washing machine where figure 3 teaches camera 182 of camera assembly 180 may be oriented such that it has a field of view that encompasses the flow of wash fluid 178 as dispensed from discharge nozzle 176. The images may be used to determine whether the flow of wash fluid 178 contains detergent or other additives.[0056]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to provide a camera and nozzle configuration as taught in Yoon to accurately and efficiently identify issues with the detergent dispenser.[0004]
Kim in view of Yoon is also silent to the controller being configured to: initiate a bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive.
Gallagher is directed towards a washing machine having a special flushing cycle for the pump 126 would need to be executed prior to switching chemistries to avoid contamination  thereby reading on the controller being configured to initiate a bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive.[0184]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to provide the operation of Gallagher in the washing machine of Kim and Yoon to provide a avoid contamination when switching chemistries.[0184]
Regarding claim 2, Kim teaches the washing machine 1 according to the embodiment of the present invention may recognize presence of residual detergent (detergent bubble) from image data using an image recognition model based on artificial intelligence and perform control to perform the additional rinsing cycle based on the result of recognition, thereby more efficiently performing the rinsing cycle of the washing operation thereby suggesting the controller is further configured to: determine that the additive content exceeds the predetermined additive threshold; and continue the bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive.[0016]
Regarding claim 4, Kim teaches when the rinsing cycle refers to a cycle for discharging detergent and contaminants from a tub or a drum to the outside. After the rinsing cycle is completed, it is preferable that residual detergent is not present in the tub or the drum. However, in a conventional washing machine, detergent may remain in the laundry or the drum (the internal glass of a door) even after the rinsing cycle is completed. When the washing operation is completed and the laundry is dried in a state in which the detergent remains, the laundry may deteriorate or the skin of a user may be damaged thereby reading on wherein terminating the bulk flush operation comprises stopping the flow of wash fluid using the water supply and stopping the dispensing of the wash additive using the bulk dispenser.[0006]
Regarding claim 5, Gallagher teaches a bubble detection sensor was described for determining when the bulk container 120 is empty or nearing empty thereby suggesting it to be an obvious modification wherein terminating the bulk flush operation comprises providing a user notification that a bulk dispensing tank is empty.[0185]
Regarding claim 6, Kim teaches output unit 330 may include an output portion for notifying the user of a variety of information related to operation of the washing machine 1. For example, the output unit 330 may include a speaker or a buzzer as an audio output unit, and include a display as a graphic or text output unit. For example, the output unit 330 may be disposed on the control panel 20 of FIG. 1, without being limited thereto, thereby reading on a user interface panel, wherein the user notification is provided through the user interface panel.[0059]
Regarding claim 7, Kim teaches communication unit 350 may include at least one communication module for connecting the washing machine 1 to a server or a user terminal. For example, the communication unit 350 may include a short-range communication module such as Bluetooth or near field communication (NFC), a wireless Internet module such as Wi-Fi, or a mobile communication module. The controller 360 may transmit the state information or operation information of the washing machine 1 to the server or the terminal through the communication unit 350 thereby reading on the controller is in operative communication with a remote device through an external network, and wherein the user notification is provided through the remote device.[0062]
Regarding claim 8, Kim teaches the detergent bubble recognizer 110 may recognize presence of detergent bubble from the image data acquired by the image sensor 322 using an image recognition model based on machine learning as a field of artificial intelligence. For example, the image recognition model may include a software or hardware artificial neural network (ANN). For example, the artificial neural network may include a convolutional neural network (CNN) learned through deep learning, a recurrent neural network (RNN), and a deep neural network (DNN) such as a deep belief network (DBN) thereby reading on the image recognition process is a machine learning image recognition process comprising at least one of a convolution neural network (CNN), a region-based convolution neural network (R-CNN), a deep belief network (DBN), or a deep neural network (DNN) image recognition process.[0074]
Regarding claim 11, Kim teaches communication unit 350 may include at least one communication module for connecting the washing machine 1 to a server or a user terminal. For example, the communication unit 350 may include a short-range communication module such as Bluetooth or near field communication (NFC), a wireless Internet module such as Wi-Fi, or a mobile communication module. The controller 360 may transmit the state information or operation information of the washing machine 1 to the server or the terminal through the communication unit 350 thereby suggesting the controller is in operative communication with a remote device through an external network, wherein the remote device comprises the camera assembly.[0062]
Regarding claim 12, Gallagher figure 21-26 teach the washing machine appliance is a vertical axis washing machine appliance.[0143]
Regarding claim 13, Gallagher teaches a method of operating a washing machine appliance, the washing machine appliance comprising a wash basket (10 drum) rotatably mounted within a wash tub (8 tub) and defining a wash chamber configured for receiving a load of clothes, a water supply (30 water supply part) configured to selectively dispense a flow of wash fluid into the wash tub, and a bulk dispenser(60 detergent supply part) for selectively adding a wash additive to the flow of wash fluid.[0037-41][0054-57]
Kim teaches a detergent bubble recognizer 110 for recognizing presence of detergent bubble from image data. The detergent bubble recognizer 110 may recognize presence of detergent bubble from the image data acquired by the image sensor 322 using an image recognition model based on machine learning as a field of artificial intelligence.  as an example of artificial intelligence technology applied to the detergent bubble recognizer 110 of the additional rinsing control module 100. The additional rinsing control module 100 may include a bubble ratio calculator 130 for calculating the ratio of detergent bubble based on the detergent bubble region extracted from the image data. The additional rinsing control module 100 may include an additional rinsing setting unit 140 for setting an additional rinsing cycle based on the ratio of detergent bubble calculated by the bubble ratio calculator 130. The additional rinsing setting unit 140 may set the number of times of performing the additional rinsing cycle and/or a time when the additional rinsing cycle is performed based on the calculated ratio of detergent bubble. The washing machine 1 according to the embodiment of the present invention may recognize presence of residual detergent (detergent bubble) from image data using an image recognition model based on artificial intelligence and perform control to perform the additional rinsing cycle based on the result of recognition, thereby more efficiently performing the rinsing cycle of the washing operation thereby reading obtaining one or more images of the flow of wash fluid using a camera assembly; analyzing the one or more images using an image recognition process to determine an additive content of the flow of wash fluid; determining that the additive content falls below a predetermined additive threshold; and terminating the bulk flush operation in response to determining that the additive content falls below the predetermined additive threshold.
Kim is silent to a discharge nozzle and obtaining one or more images of the flow of wash fluid using a camera assembly being mounted to face the discharge nozzle to monitor the flow of wash fluid before the wash fluid reaches the load of clothes.
Yoon is directed towards a washing machine where figure 3 teaches camera 182 of camera assembly 180 may be oriented such that it has a field of view that encompasses the flow of wash fluid 178 as dispensed from discharge nozzle 176. The images may be used to determine whether the flow of wash fluid 178 contains detergent or other additives.[0056]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to provide a camera and nozzle configuration as taught in Yoon to accurately and efficiently identify issues with the detergent dispenser.[0004]
Kim in view of Yoon  is also silent to initiating a bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive.
Gallagher is directed towards a washing machine having a special flushing cycle for the pump 126 would need to be executed prior to switching chemistries to avoid contamination  thereby reading on the controller being configured to initiate a bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive.[0184]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to provide the operation of Gallagher in the washing machine of Kim and Yoon to provide a avoid contamination when switching chemistries.[0184]
Regarding claim 14, Kim teaches the washing machine 1 according to the embodiment of the present invention may recognize presence of residual detergent (detergent bubble) from image data using an image recognition model based on artificial intelligence and perform control to perform the additional rinsing cycle based on the result of recognition, thereby more efficiently performing the rinsing cycle of the washing operation thereby suggesting determining that the additive content exceeds the predetermined additive threshold; and continuing the bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive.[0016]
Regarding claim 16, Kim teaches when the rinsing cycle refers to a cycle for discharging detergent and contaminants from a tub or a drum to the outside. After the rinsing cycle is completed, it is preferable that residual detergent is not present in the tub or the drum. However, in a conventional washing machine, detergent may remain in the laundry or the drum (the internal glass of a door) even after the rinsing cycle is completed. When the washing operation is completed and the laundry is dried in a state in which the detergent remains, the laundry may deteriorate or the skin of a user may be damaged thereby reading wherein terminating the bulk flush operation comprises stopping the flow of wash fluid using the water supply and stopping the dispensing of the wash additive using the bulk dispenser.[0006]
Regarding claim 17, Gallagher teaches a bubble detection sensor was described for determining when the bulk container 120 is empty or nearing empty thereby suggesting it to be an obvious modification wherein terminating the bulk flush operation comprises providing a user notification that a bulk dispensing tank is empty.[0185]
Regarding claim 18, Kim teaches the detergent bubble recognizer 110 may recognize presence of detergent bubble from the image data acquired by the image sensor 322 using an image recognition model based on machine learning as a field of artificial intelligence. For example, the image recognition model may include a software or hardware artificial neural network (ANN). For example, the artificial neural network may include a convolutional neural network (CNN) learned through deep learning, a recurrent neural network (RNN), and a deep neural network (DNN) such as a deep belief network (DBN) thereby reading on wherein the image recognition process is a machine learning image recognition process comprising at least one of a convolution neural network (CNN), a region-based convolution neural network (R-CNN), a deep belief network (DBN), or a deep neural network (DNN) image recognition process.[0074]
Regarding claim 20, Kim teaches communication unit 350 may include at least one communication module for connecting the washing machine 1 to a server or a user terminal. For example, the communication unit 350 may include a short-range communication module such as Bluetooth or near field communication (NFC), a wireless Internet module such as Wi-Fi, or a mobile communication module. The controller 360 may transmit the state information or operation information of the washing machine 1 to the server or the terminal through the communication unit 350 thereby suggesting the washing machine appliance is in operative communication with a remote device through an external network, wherein the remote device comprises the camera assembly.[0062]
Claim(s) 3 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 2020/0071871), Yoon (US 2023/0067550), and Gallagher (US 2017/0037559), as applied to claim 1, and in further view of Ha (US 2021/0025104).
Regarding claim 3, Kim in view of Yoon and Gallagher is silent to the controller is further configured to: analyze the one or more images using the image recognition process to determine that a flow rate of the flow of wash fluid falls below a flow rate threshold; and provide a user notification that there is a fault condition with the water supply.
Ha is directed towards an apparatus and method for detecting water supply in a household appliance wherein FIGS. 6B to 6E are exemplary views illustrating a state in which water supply detection image data designates the water supplier in the tub as the water supply region of interest based on data having 3 to 5 second images (30 frames per second) and uses the optical flow algorithm as the vision algorithm. Referring to FIGS. 6B to 6E, the optical flow is detected, and a magnitude of the optical vector has a minimum threshold value of 208 and is detected from 208 or more. The optical vector is represented by a set of dots at the part at the upper end region of the tub where the water is supplied, the optical vector means that the darker and wider the optical vector, the more washing water is supplied. In addition, when the water supply is stably performed, the optical vector has a wide distribution and the vector values are arranged in the region of interest.[0087-93]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to provide the apparatus and method of Ha in the washing machine of Kim, Yoon, and Gallagher to monitor the blockage in the water supply path.[0002]
Regarding claim 15, Ha is directed towards an apparatus and method for detecting water supply in a household appliance used for water supply equipment that needs to monitor the blockage in the water supply path. FIGS. 6B to 6E are exemplary views illustrating a state in which water supply detection image data designates the water supplier in the tub as the water supply region of interest based on data having 3 to 5 second images (30 frames per second) and uses the optical flow algorithm as the vision algorithm. Referring to FIGS. 6B to 6E, the optical flow is detected, and a magnitude of the optical vector has a minimum threshold value of 208 and is detected from 208 or more. The optical vector is represented by a set of dots at the part at the upper end region of the tub where the water is supplied, the optical vector means that the darker and wider the optical vector, the more washing water is supplied. In addition, when the water supply is stably performed, the optical vector has a wide distribution and the vector values are arranged in the region of interest thereby reading on analyzing the one or more images using the image recognition process to determine that a flow rate of the flow of wash fluid falls below a flow rate threshold; and providing a user notification that there is a fault condition with the water supply.[0002][0087-93]
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.

APPLICANT REBUTTAL: REMARKSClaims 1-8, 11-18, and 20 are pending in the present application including independent claims 1 and 13. Applicants respectfully request reconsideration and allowance in view of the amendments to the claims and the following remarks.Claim Reiections - 35 U.S.C. § 103 Claims 1-2, 4-8, 11-14, 16-28, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Kim (US 2020/0071871) in view of Yoon (US 2023/0067550) and Gallagher (US 2017/0037559). Claims 3 and 15 are rejected under 35 U.S.C. § 103 as being unpatentable over Kim in view of Yoon and Gallagher, and further in view of Ha (US 2021/0025104). These rejections are respectfully traversed. In this regard, Applicant respectfully submits that these claims are not obvious for at least the reason that the proffered combinations do not yield the claimed invention.Independent claim 1 currently recites that the camera assembly is mounted to face the discharge nozzle to monitor the flow of wash fluid before the wash fluid reaches the load of clothes. The Office Action acknowledges that Kim is silent as to such a construction but contends that Yoon cures this deficiency. Office Action, page 4. However, for the reasons described below, Applicant respectfully submits that this is not a proper combination of references and that claim 1 is patentable over the cited references.Applicant respectfully submits that Yoon is not a proper prior art reference for use in supporting the present obviousness rejection. In this regard, Applicant notes that Yoon was published on March 2, 2023, after the October 10, 2022 filing date of the present application.Therefore, Yoon could only qualify as prior art under 35 U.S.C. § 102(a)(2). However, 35 U.S.C.§ 102(b)(2) provides exceptions to qualifying prior art under that section. Specifically, relevant here is 35 U.S.C. § 102(b)(2)(C), which states that a disclosure is not prior art under (a)(2) if "the subject matter disclosed and the claimed invention, not later than the effective filing date of the claimed invention, were owned by the same person . . .." Applicant refers Examiner to the Assignment recorded at reel/frame 057354/0063, executed on September 1, 2021 (prior to the effective filing date of the claimed invention), assigning Yoon to Haier US Appliance Solutions, Inc., the same assignee as the present application. In view of this assignment, Applicant respectfully submits that Yoon is not prior art to the present application.In view of the above, the Examiner is respectfully requested to reconsider the rejection of independent claim 1. Applicant respectfully submits that the cited references fail to teach all limitations of independent claim 1 and that this claim patentably defines over the cited references. The Office Action rejected independent claim 13 on the same grounds and based upon the same authority as claim 1. Notably, independent claim 13 has been amended to include similar limitations as claim 1. Thus, for the same reasons as discussed with respect to claim 1, Applicant respectfully submits that claim 13 is patentable over the cited references.Consequently, it is respectfully submitted that independent claims 1 and 13 are in condition for allowance.Applicant also respectfully submits that for at least the reasons indicated above relating to independent claims 1 and 13, the corresponding dependent claims are also in condition for allowance. However, Applicant also notes that the patentability of corresponding dependent claims certainly does not hinge on the patentability of the independent claims. In particular, it is believed that some or all of these other claims may possess features that are independently patentable, regardless of the patentability of the independent claims. In summary, it is respectfully submitted that the present application is in complete condition for allowance and favorable action is therefore respectfully requested.Conclusion With the present submission, Applicant respectfully submits that all pending claims are allowable, and that the application is in condition for allowance. Favorable action thereon is respectfully requested. The Examiner is encouraged to contact the undersigned if there are any questions regarding this matter or if any additional information is required. If any fee not accounted for above is required for entry of this Response or papers filed herewith, authorization is hereby granted to charge such fee to Deposit Account No. 04-1403. If any petition is required for entry, such petition is hereby made and any associated fees may also be charged to our deposit account.[SIGNATURE BLOCK ON FOLLOWING PAGE]Respectfully submitted, DORITY & MANNING, P.A.July 8, 2025

NEXT OFFICE ACTION: DETAILED ACTION
Reasons for Allowance
Allowable Subject Matter
The following is an examiner’s statement of reasons for allowance: the closest prior art of record Kim (US 2020/0071871) in view of Gallagher (US 2017/0037559) fails to teach or suggest the claimed invention.
Kim figures 1-4 teach a washing machine appliance, comprising:
a wash tub (8 tub) positioned within a cabinet (2);
a wash basket (10 drum) rotatably mounted within the wash tub and defining a wash chamber configured for receiving a load of clothes;
a water supply (30 water supply part) configured to selectively dispense a flow of wash fluid into the wash tub;
a bulk dispenser (60 detergent supply part) for selectively adding a wash additive to the flow of wash fluid;
a controller (360) operably coupled to the water supply, the bulk dispenser, and a camera assembly (322 image sensor).[0037-41][0054-57]
Kim teaches a detergent bubble recognizer 110 for recognizing presence of detergent bubble from image data. The detergent bubble recognizer 110 may recognize presence of detergent bubble from the image data acquired by the image sensor 322 using an image recognition model based on machine learning as a field of artificial intelligence.  As an example of artificial intelligence technology applied to the detergent bubble recognizer 110 of the additional rinsing control module 100. The additional rinsing control module 100 may include a bubble ratio calculator 130 for calculating the ratio of detergent bubble based on the detergent bubble region extracted from the image data. The additional rinsing control module 100 may include an additional rinsing setting unit 140 for setting an additional rinsing cycle based on the ratio of detergent bubble calculated by the bubble ratio calculator 130. The additional rinsing setting unit 140 may set the number of times of performing the additional rinsing cycle and/or a time when the additional rinsing cycle is performed based on the calculated ratio of detergent bubble. The washing machine 1 according to the embodiment of the present invention may recognize presence of residual detergent (detergent bubble) from image data using an image recognition model based on artificial intelligence and perform control to perform the additional rinsing cycle based on the result of recognition, thereby more efficiently performing the rinsing cycle of the washing operation. Kim teaches image sensor 322 may be included in at least one camera installed inside or outside the washing machine 1 thereby reading on the controller being configured to obtain one or more images of the flow of wash fluid using the camera assembly, analyze the one or more images using an image recognition process to determine an additive content of the flow of wash fluid; determine that the additive content falls below a predetermined additive threshold; and terminate the bulk flush operation in response to determining that the additive content falls below the predetermined additive threshold.[0055,0074, 0077, 0110, 0112-113, 0116]
Kim is silent to a discharge nozzle. Kim is also silent to the controller being configured to: initiate a bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive.
Gallagher is directed towards a washing machine wherein the mixture of water and detergent outputted by the pump 126 can be sprayed from the nozzle 66 generally directly down and along a side of the basket while the basket is being rotated. Gallagher further teaches the washing machine having a special flushing cycle for the pump 126 would need to be executed prior to switching chemistries to avoid contamination   thereby reading on a discharge nozzle and the controller being configured to initiate a bulk flush operation by operating the water supply to provide the flow of wash fluid and the bulk dispenser to dispense the wash additive.[0141][0184]
However, Kim in view of Gallagher is silent to the camera assembly mounted within the cabinet above the wash chamber and faces the discharge nozzle to monitor the flow of wash fluid, and the controller configured to obtain one or more images of the flow of wash fluid using the camera assembly before the wash fluid reaches the load of clothes.
Conclusion
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”

CITED REFERENCE SUMMARIES: Ref US20230067550A1: Claims:
A washing machine appliance, comprising: a wash tub positioned within a cabinet; a wash basket rotatably mounted within the wash tub and defining a wash chamber configured for receiving a load of clothes; a dispensing assembly configured to selectively dispense a flow of wash fluid through a discharge nozzle into the wash tub; a camera assembly mounted within the cabinet in view of the flow of wash fluid; and a controller operably coupled to the camera assembly, the controller being configured to: operate the dispensing assembly to discharge the flow of wash fluid into the wash tub; obtain one or more images of the flow of wash fluid using the camera assembly; analyze the one or more images using a machine learning image recognition process to determine an actual detergent content of the flow of wash fluid; determine that the actual detergent content is different than a target detergent content; and implement a responsive action in response to determining that the actual detergent content is different than the target detergent content.
a wash tub positioned within a cabinet;
a wash basket rotatably mounted within the wash tub and defining a wash chamber configured for receiving a load of clothes;
a dispensing assembly configured to selectively dispense a flow of wash fluid through a discharge nozzle into the wash tub;
a camera assembly mounted within the cabinet in view of the flow of wash fluid; and
a controller operably coupled to the camera assembly, the controller being configured to: operate the dispensing assembly to discharge the flow of wash fluid into the wash tub; obtain one or more images of the flow of wash fluid using the camera assembly; analyze the one or more images using a machine learning image recognition process to determine an actual detergent content of the flow of wash fluid; determine that the actual detergent content is different than a target detergent content; and implement a responsive action in response to determining that the actual detergent content is different than the target detergent content.
operate the dispensing assembly to discharge the flow of wash fluid into the wash tub;
obtain one or more images of the flow of wash fluid using the camera assembly;
analyze the one or more images using a machine learning image recognition process to determine an actual detergent content of the flow of wash fluid;
determine that the actual detergent content is different than a target detergent content; and
implement a responsive action in response to determining that the actual detergent content is different than the target detergent content.
The washing machine appliance of claim 1 , wherein the target detergent content is a predetermined detergent volume during a wash cycle.
The washing machine appliance of claim 2 , wherein the predetermined detergent volume is based on a load size and a load type of the load of clothes.
The washing machine appliance of claim 1 , wherein the target detergent content is no detergent during a rinse cycle.
The washing machine appliance of claim 1 , wherein determining that the actual detergent content is different than the target detergent content comprises: monitoring the flow of wash fluid to determine an actual detergent dispense duration while the flow of wash fluid is being dispensed; and estimating a dispensed detergent volume from the actual detergent dispense duration.
monitoring the flow of wash fluid to determine an actual detergent dispense duration while the flow of wash fluid is being dispensed; and
estimating a dispensed detergent volume from the actual detergent dispense duration.
The washing machine appliance of claim 1 , wherein the controller is further configured to: determine that the actual detergent content is identical to the target detergent content; and proceed with an operating cycle in response to determining that the actual detergent content is identical to the target detergent content.
determine that the actual detergent content is identical to the target detergent content; and
proceed with an operating cycle in response to determining that the actual detergent content is identical to the target detergent content.
The washing machine appliance of claim 1 , wherein implementing the responsive action comprises: providing a user notification that the actual detergent content is different than the target detergent content.
providing a user notification that the actual detergent content is different than the target detergent content.
The washing machine appliance of claim 7 , further comprising: a user interface panel, wherein the user notification is provided through the user interface panel.
a user interface panel, wherein the user notification is provided through the user interface panel.
The washing machine appliance of claim 7 , wherein the controller is in operative communication with a remote device through an external network, and wherein the user notification is provided through the remote device.
The washing machine appliance of claim 1 , wherein the machine learning image recognition process comprises at least one of a convolution neural network (âCNNâ), a region-based convolution neural network (âR-CNNâ), a deep belief network (âDBNâ), or a deep neural network (âDNNâ) image recognition process.
The washing machine appliance of claim 1 , wherein the camera assembly includes a light source for illuminating the wash chamber while obtaining the one or more images.
The washing machine appliance of claim 1 , wherein the camera assembly is mounted above the wash chamber and faces the discharge nozzle or the wash chamber.
The washing machine appliance of claim 1 , further comprising: a drain pump assembly fluidly coupled to the wash tub for selectively draining wash fluid from the wash tub, wherein implementing the responsive action comprises draining the wash tub.
a drain pump assembly fluidly coupled to the wash tub for selectively draining wash fluid from the wash tub, wherein implementing the responsive action comprises draining the wash tub.
The washing machine appliance of claim 1 , wherein the washing machine appliance is a vertical axis washing machine appliance.
A method of operating a washing machine appliance, the washing machine appliance comprising a wash basket rotatably mounted within a wash tub and defining a wash chamber configured for receiving a load of clothes, a dispensing assembly configured to selectively dispense a flow of wash fluid through a discharge nozzle into the wash tub, and a camera assembly mounted within in view of the flow of wash fluid, the method comprising: operating the dispensing assembly to discharge the flow of wash fluid into the wash tub; obtaining one or more images of the flow of wash fluid using the camera assembly; analyzing the one or more images using a machine learning image recognition process to determine an actual detergent content of the flow of wash fluid; determining that the actual detergent content is different than a target detergent content; and implementing a responsive action in response to determining that the actual detergent content is different than the target detergent content.
operating the dispensing assembly to discharge the flow of wash fluid into the wash tub;
obtaining one or more images of the flow of wash fluid using the camera assembly;
analyzing the one or more images using a machine learning image recognition process to determine an actual detergent content of the flow of wash fluid;
determining that the actual detergent content is different than a target detergent content; and
implementing a responsive action in response to determining that the actual detergent content is different than the target detergent content.
The method of claim 15 , wherein the target detergent content is a predetermined detergent volume during a wash cycle and no detergent during a rinse cycle.
The method of claim 15 , wherein determining that the actual detergent content is different than the target detergent content comprises: monitoring the flow of wash fluid to determine an actual detergent dispense duration while the flow of wash fluid is being dispensed; and estimating a dispensed detergent volume from the actual detergent dispense duration.
The method of claim 15 , further comprising: determining that the actual detergent content is identical to the target detergent content; and proceeding with an operating cycle in response to determining that the actual detergent content is identical to the target detergent content.
determining that the actual detergent content is identical to the target detergent content; and
proceeding with an operating cycle in response to determining that the actual detergent content is identical to the target detergent content.
The method of claim 15 , wherein implementing the responsive action comprises: providing a user notification that the actual detergent content is different than the target detergent content.
The method of claim 15 , wherein the machine learning image recognition process comprises at least one of a convolution neural network (âCNNâ), a region-based convolution neural network (âR-CNNâ), a deep belief network (âDBNâ), or a deep neural network (âDNNâ) image recognition process.
Ref US20230067550: Claims:
1. A washing machine appliance, comprising:
a wash tub positioned within a cabinet; a wash basket rotatably mounted within the wash tub and defining a wash chamber configured for receiving a load of clothes; a dispensing assembly configured to selectively dispense a flow of wash fluid through a discharge nozzle into the wash tub; a camera assembly mounted within the cabinet in view of the flow of wash fluid; and a controller operably coupled to the camera assembly, the controller being configured to:
operate the dispensing assembly to discharge the flow of wash fluid into the wash tub;
obtain one or more images of the flow of wash fluid using the camera assembly;
analyze the one or more images using a machine learning image recognition process to determine an actual detergent content of the flow of wash fluid;
determine that the actual detergent content is different than a target detergent content; and
implement a responsive action in response to determining that the actual detergent content is different than the target detergent content.
2. The washing machine appliance of claim 1, wherein the target detergent content is a predetermined detergent volume during a wash cycle.
3. The washing machine appliance of claim 2, wherein the predetermined detergent volume is based on a load size and a load type of the load of clothes.
4. The washing machine appliance of claim 1, wherein the target detergent content is no detergent during a rinse cycle.
5. The washing machine appliance of claim 1, wherein determining that the actual detergent content is different than the target detergent content comprises:
monitoring the flow of wash fluid to determine an actual detergent dispense duration while the flow of wash fluid is being dispensed; and estimating a dispensed detergent volume from the actual detergent dispense duration.
6. The washing machine appliance of claim 1, wherein the controller is further configured to:
determine that the actual detergent content is identical to the target detergent content; and proceed with an operating cycle in response to determining that the actual detergent content is identical to the target detergent content.
7. The washing machine appliance of claim 1, wherein implementing the responsive action comprises:
providing a user notification that the actual detergent content is different than the target detergent content.
8. The washing machine appliance of claim 7, further comprising:
a user interface panel, wherein the user notification is provided through the user interface panel.
9. The washing machine appliance of claim 7, wherein the controller is in operative communication with a remote device through an external network, and wherein the user notification is provided through the remote device.
10. The washing machine appliance of claim 1, wherein the machine learning image recognition process comprises at least one of a convolution neural network (“CNN”), a region-based convolution neural network (“R-CNN”), a deep belief network (“DBN”), or a deep neural network (“DNN”) image recognition process.
11. The washing machine appliance of claim 1, wherein the camera assembly includes a light source for illuminating the wash chamber while obtaining the one or more images.
12. The washing machine appliance of claim 1, wherein the camera assembly is mounted above the wash chamber and faces the discharge nozzle or the wash chamber.
13. The washing machine appliance of claim 1, further comprising:
a drain pump assembly fluidly coupled to the wash tub for selectively draining wash fluid from the wash tub, wherein implementing the responsive action comprises draining the wash tub.
14. The washing machine appliance of claim 1, wherein the washing machine appliance is a vertical axis washing machine appliance.
15. A method of operating a washing machine appliance, the washing machine appliance comprising a wash basket rotatably mounted within a wash tub and defining a wash chamber configured for receiving a load of clothes, a dispensing assembly configured to selectively dispense a flow of wash fluid through a discharge nozzle into the wash tub, and a camera assembly mounted within in view of the flow of wash fluid, the method comprising:
operating the dispensing assembly to discharge the flow of wash fluid into the wash tub; obtaining one or more images of the flow of wash fluid using the camera assembly; analyzing the one or more images using a machine learning image recognition process to determine an actual detergent content of the flow of wash fluid; determining that the actual detergent content is different than a target detergent content; and implementing a responsive action in response to determining that the actual detergent content is different than the target detergent content.
16. The method of claim 15, wherein the target detergent content is a predetermined detergent volume during a wash cycle and no detergent during a rinse cycle.
17. The method of claim 15, wherein determining that the actual detergent content is different than the target detergent content comprises:
monitoring the flow of wash fluid to determine an actual detergent dispense duration while the flow of wash fluid is being dispensed; and estimating a dispensed detergent volume from the actual detergent dispense duration.
18. The method of claim 15, further comprising:
determining that the actual detergent content is identical to the target detergent content; and proceeding with an operating cycle in response to determining that the actual detergent content is identical to the target detergent content.
19. The method of claim 15, wherein implementing the responsive action comprises:
providing a user notification that the actual detergent content is different than the target detergent content.
20. The method of claim 15, wherein the machine learning image recognition process comprises at least one of a convolution neural network (“CNN”), a region-based convolution neural network (“R-CNN”), a deep belief network (“DBN”), or a deep neural network (“DNN”) image recognition process.
Ref US20200071871: Claims:
1. A washing machine comprising:
a door comprising an external cover and an internal glass configured to open and close a laundry entrance of the washing machine; at least one image sensor disposed to face the internal glass and configured to acquire image data; a detergent bubble recognizer configured to recognize detergent bubbles from the image data through a machine learning based image recognition model; and a controller configured to control the washing machine to perform an additional rinsing cycle based on a result of recognition.
2. The washing machine of claim 1, further comprising a bubble ratio calculator configured to process the image data and to calculate a ratio of an area of the detergent bubbles to an area of the internal glass, based on the detergent bubbles being recognized by the detergent bubble recognizer,
wherein the controller is configured to control the washing machine to perform the additional rinsing cycle based on the ratio calculated by the bubble ratio calculator.
3. The washing machine of claim 2, wherein the controller is further configured to update the machine learning based image recognition model of the detergent bubble recognizer using the acquired image data, and at least one of (i) information regarding an amount of laundry, (ii) information regarding an amount of detergent, or (iii) washing mode information.
4. The washing machine of claim 2, wherein the controller is further configured to set a number of times for performing the additional rinsing cycle based on the ratio calculated by the bubble ratio calculator.
5. The washing machine of claim 4, wherein the controller is further configured to:
set the number of times for performing the additional rinsing cycle to a first number based on the calculated ratio being a first ratio, and set the number of times for performing the additional rinsing cycle to a second number based on the calculated ratio being a second ratio greater than the first ratio.
6. The washing machine of claim 2, wherein the controller is further configured to increase a duration of time for performing the additional rinsing cycle, as the calculated ratio increases.
7. The washing machine of claim 1, wherein the controller is further configured to control the at least one image sensor to acquire the image data based on completion of a first rinsing cycle prior to the additional rinsing cycle.
8. The washing machine of claim 7, wherein the first rinsing cycle is one of a plurality of cycles in a washing operation of the washing machine, and
wherein the controller is further configured to perform a next cycle that follows the first rinsing cycle in the washing operation, based on the detergent bubbles not being recognized from the image data by the detergent bubble recognizer.
9. The washing machine of claim 1, wherein the at least one image sensor is disposed between the external cover and the internal glass of the door.
10. The washing machine of claim 9, wherein the at least one image sensor is fastened to an internal wall of the external cover of the door.
11. The washing machine of claim 1, further comprising at least one light source provided on an inner circumferential surface of the laundry entrance,
wherein the controller is configured to activate the at least one light source during acquisition of the image data by the at least one image sensor.
12. The washing machine of claim 1, wherein the image recognition model comprises a deep neural network that is configured to implement machine-based deep learning.
13. The washing machine of claim 2, further comprising a communication unit configured to transmit, to a server or a terminal, the acquired image data or at least one of (i) a number of times for performing the additional rinsing cycle, or (ii) duration of time for performing the additional rinsing cycle based on the ratio calculated by the bubble ratio calculator.
14. The washing machine of claim 13, wherein the controller is further configured to:
receive updated learning data from the server through the communication unit; and update the machine learning based image recognition model of the detergent bubble recognizer based on the received updated learning data.
15. A method of operating a washing machine, the method comprising:
performing a first rinsing cycle of a washing operation of laundry; acquiring image data of an internal glass of a door of the washing machine, based on completion of the first rinsing cycle; recognizing detergent bubbles from the image data through a machine learning base
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