Office Action 1: Non-Final Rejection (2025-04-14)
Rejection types: 101 - Patent-Eligible Subject Matter, 112 - Written Description/Enablement

CLAIMS:
WHAT IS CLAIMED IS:

1\. A laundry treatment appliance defining a vertical direction, a lateral direction, and a transverse direction, the laundry treatment appliance comprising:
a cabinet defining a receiving space;
a wash tub provided within the receiving space;
a wash basket received within the wash tub; and
a controller provided within the cabinet, the controller being configured to direct a laundry operation, the laundry operation comprising:
receiving a plurality of wash requests from a plurality of unique users, wherein each wash request of the plurality of wash requests comprises an image of a unique laundry load;
analyzing, by one or more computing devices using a machine learning image recognition model, a first image to evaluate one or more laundry load characteristics of a first unique laundry load;
analyzing, by the one or more computing devices using the machine learning image recognition model, a second image to evaluate one or more laundry load characteristics of a second unique laundry load;
matching a first unique user and a second unique user from the plurality of users, the first user and the second user having a matching laundry load characteristic from the one or more laundry load characteristics; and
generating a joint washing cycle subsequent to matching the first and second unique users based on the matching laundry load characteristics.

2\. The laundry treatment appliance of claim 1, wherein the laundry operation further comprises:
receiving the first unique laundry load within the wash basket;
determining a first unique laundry load size of the first unique laundry load;
receiving the second unique laundry load within the wash basket after determining the first unique laundry load size;
determining a second unique laundry load size of the second unique laundry load; and determining a first individual cost for the first unique user and a second individual cost for the second unique user based on the first and second unique laundry load sizes.

3\. The laundry treatment appliance of claim 2, wherein determining the first and second unique laundry load sizes comprises:
capturing a first laundry load image of the first unique laundry load;
analyzing, by the one or more computing devices using the machine learning image recognition model, the first laundry load image to determine a first occupied space within the wash basket;
capturing a second laundry load image of the first and second unique laundry loads within the wash basket; and
analyzing, by the one or more computing devices using the machine learning image recognition model, the second laundry load image to determine a second occupied space within the wash basket.

4\. The laundry treatment appliance of claim 3, wherein the laundry operation further comprises:
determining a total cost for the joint washing operation in response to determining the second occupied space within the wash basket.

5\. The laundry treatment appliance of claim 4, wherein the operation further comprises:
determining a first individual cost for the first unique user and a second unique cost for the second unique user after determining the total cost of the joint washing operation;
receiving a confirmation from each of the first and second unique users; and
initiating the joint washing cycle in response to receiving the confirmation from each of the first and second unique users.
6\. The laundry treatment appliance of claim 3, wherein the first and second laundry load images are captured by a remote camera outside of the wash basket.
7\. The laundry treatment appliance of claim 1, wherein the machine learning image recognition model 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.

8\. The laundry treatment appliance of claim 1, wherein the one or more laundry load characteristics comprises a color of the laundry load.
9\. The laundry treatment appliance of claim 8, wherein the laundry operation further comprises:
determining, via image analysis on a first unique image, a first unique laundry load is a first color after receiving the wash requests; and
concealing the first laundry load in the first unique image with a first reference image in response to determining the first unique laundry load is the first color.

10\. The laundry treatment appliance of claim 9, wherein determining the first unique laundry load is the first color comprises:
determining that at least 80% of the first unique laundry load in the first unique image is not white.

11\. A method of operating a laundry treatment appliance, the laundry treatment appliance comprising a wash basket for receiving one or more laundry loads, the method comprising:
receiving wash requests from a plurality of unique users, wherein each of the wash requests comprises an image of a unique laundry load; analyzing, by one or more computing devices using a machine learning image recognition model, a first image to evaluate one or more laundry load characteristics of a first unique laundry load;
analyzing, by the one or more computing devices using the machine learning image recognition model, a second image to evaluate one or more laundry load characteristics of a second unique laundry load;
matching a first unique user and a second unique user from the plurality of users, the first user and the second user having a matching laundry load characteristic from the one or more laundry load characteristics;
determining a first unique laundry load size of the first unique user, the first unique laundry load size being determined within the wash basket;
determining a second unique laundry load size of the second unique user, the second unique laundry load size being determined within the wash basket after determining the first unique laundry load size; and
generating a joint washing cycle subsequent to determining the first and second laundry load sizes, the joint washing cycle being based on the matching laundry load characteristics.

12\. The method of claim 11, wherein determining the first and second unique laundry load sizes comprises:
receiving the first unique laundry load within the wash basket;
capturing a first laundry load image of the first unique laundry load;
analyzing, by the one or more computing devices using the machine learning image recognition model, the first laundry load image to determine a first occupied space within the wash basket;
receiving the second unique laundry load within the wash basket after determining the first unique laundry load size;
capturing a second laundry load image of the first and second unique laundry loads within the wash basket; and
analyzing, by the one or more computing devices using the machine learning image recognition model, the second laundry load image to determine a second occupied space within the wash basket.

13\. The method of claim 12, wherein the laundry operation further comprises:
determining a total cost for the joint washing operation in response to determining the second occupied space within the wash basket.

14\. The method of claim 13, wherein the operation further comprises:
determining a first individual cost for the first unique user and a second unique cost for the second unique user after determining the total cost of the joint washing operation;
receiving a confirmation from each of the first and second unique users; and
initiating the joint washing cycle in response to receiving the confirmation from each of the first and second unique users.

15\. The method of claim 12, wherein the first and second laundry load images are captured by a remote camera outside of the wash basket.

16\. The method of claim 11, wherein the machine learning image recognition model 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.

17\. The method of claim 11, wherein the one or more laundry load characteristics comprises a color of the laundry load.

18\. The method of claim 17, wherein the laundry operation further comprises:
determining, via image analysis on a first unique image, a first unique laundry load is a first color after receiving the wash requests; and
concealing the first laundry load in the first unique image with a first reference image in response to determining the first unique laundry load is the first color.

19\. The method of claim 18. N herein determining the first unique laundry load is the first color comprises:
determining that at least 80% of the first unique laundry load in the first unique image is not white. 22 23 24 25 26 27

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 .
This communication is in response to the application filed on 10/19/2022. Claims 1-19 are pending and have been examined.
Information Disclosure Statement
The IDS dated 10/19/2022 has been considered and placed in the application file.
Specification
The specification is objected to because of the following informalities:
In paragraph 0022, line 11, “within the context of a horizontal axis and front load laundry” should read “within the context of a “horizontal axis” or “front load” laundry”
In paragraph 0023, line 2, “define opening 116 of to permit” should read “define opening 116 to permit”
In paragraph 0046, line 5, “recommendations on the covn5” should read “recommendations of the covered”
According to 37 CFR 1.71, MPEP §§ 608.01, 2161, and 2162, the specification must be in such particularity as to enable any person skilled in the pertinent art or science to make and use the invention without involving extensive experimentation and must clearly convey enough information about the invention to show that applicant invented the subject matter that is claimed. An applicant is ordinarily permitted to use his or her own terminology, as long as it can be understood. Necessary grammatical corrections, are required.  Reference characters must be properly applied, no single reference character being used for two different parts or for a given part and a modification of such part. See 37 CFR 1.84(p). Every feature specified in the claims must be illustrated, but there should be no superfluous illustrations.
A substitute specification in proper idiomatic English and in compliance with 37 CFR 1.52(a) and (b) is required. The substitute specification filed must be accompanied by a statement that it contains no new matter.
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.
Claim 4 and 13 recite the limitation joint washing operation.  There is insufficient antecedent basis for this limitation in the claim because it is unclear if “joint washing operation” is the same as “joint washing cycle” in claim 1 and 11 or if it is something different. For examination purposes, they will be interpreted to be the same. Claim 5, dependent on claim 4, and 14, dependent on claim 13, are similarly rejected.
Claim 5 and 14 recite the limitations:
the operation, there is insufficient antecedent basis for this limitation in the claim because it is unclear if “the operation” is the same as “the laundry operation” in claim 1 and 11 or if it is something different. The limitation will be interpreted as “the laundry operation” for examination.
“a first individual cost”, there is insufficient antecedent basis for this limitation in the claim because it is unclear if “a first individual cost” is the same as the “a first individual cost” in claim 2 and 12 or if it is something different. Based on the disclosure it appears that “a first individual cost” should be “the first individual cost” and will be interpreted as such for examination purposes.
a second unique cost, there is insufficient antecedent basis for this limitation in the claim because it is unclear if “a second unique cost” is the same as “a second individual cost” in claim 2 and 12 or if it is something different. Based on the disclosure it appears that “a second unique cost” should be “the second individual cost” and will be interpreted as such for examination purposes.
Claim 8 and 17 recite the limitation the laundry load.  There is insufficient antecedent basis for this limitation in the claim because it is unclear if “the laundry load” is the same as “unique laundry load”, “first unique laundry load”, or “second unique laundry load”” in claim 1 and 11 or if it is something different. For examination purposes, “the laundry load” will be interpreted as any of the loads listed. Claim 9-10, dependent on claim 8, and claim 18-19, dependent on claim 17, are similarly rejected.
Claim 9 and 18 recite the limitation the wash requests. There is insufficient antecedent basis for this limitation in the claim because it is unclear if “the wash requests” is the same as “the plurality of wash requests” in claim 1 or if it is something different. For examination purposes, they will be interpreted to be the same. Claim 10, dependent on claim 9, and claim 19, dependent on claim 18, are similarly rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion, prediction, etc.
Analysis for claim 1 is provided in the following. Claim 1 is reproduced in the following (annotation added):
a controller provided within the cabinet, the controller being configured to direct a laundry operation, the laundry operation comprising:
receiving a plurality of wash requests from a plurality of unique users, wherein each wash request of the plurality of wash requests comprises an image of a unique laundry load;
analyzing, by one or more computing devices using a machine learning image recognition model, a first image to evaluate one or more laundry load characteristics of a first unique laundry load;
analyzing, by the one or more computing devices using the machine learning image recognition model, a second image to evaluate one or more laundry load characteristics of a second unique laundry load;
matching a first unique user and a second unique user from the plurality of users, the first user and the second user having a matching laundry load characteristic from the one or more laundry load characteristics; and
generating a joint washing cycle subsequent to matching the first and second unique users based on the matching laundry load characteristics.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 1 is directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories?
YES.  Claim 1 is directed to a system i.e., a machine.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES, the claims are directed toward a mental process (i.e., abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Steps (a)-(e), as drafted, under their broadest reasonable interpretation, cover performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (‘[M]ental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’ (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
As such, a person could analyze image(s) and determine characteristics associated with information presented in the image(s) and match multiple images together based on the identified characteristics with a degree of error or lack thereof either mentally or using a pen and paper. The mere nominal recitation that the various steps are being executed by a controller (e.g., processing device) does not take the limitations out of the mental process grouping.  Thus, the claims recite a mental process.
If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with a simple tool such as a pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Thus, claim 1 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
Thus, since Claims 1 is: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, claim 1 is not eligible subject matter under 35 U.S.C 101.
Dependent claims 2-10 recite more mental processes and/or insignificant extra solution activities. Note abstract idea does not preclude using simple tools (see MPEP 2106.04 (a)(2) III). Claims 2-10 are not eligible as identified as: being directed towards an abstract idea, not reciting additional elements that integrate the judicial exception into a practical application, and not reciting additional elements that amount to significantly more than the judicial exception.
Claim 11-19 are directed to a method i.e., a process. Method claim 11 and system claim 1 are related as method and system of using the same, with each claimed element’s function corresponding to the method steps. Accordingly claim 11 is similarly rejected under the same rationale as applied above with respect to the method claim. Dependent claims 12-19 recite more mental processes and/or insignificant extra solution activities. Claims 12-19 are not eligible.
Allowable Subject Matter
Claim 1-19 would be considered allowable, but for those claims which a rejection under 35 U.S.C 101 is applied. Additionally claims 4-6, 8-10, 13-15, and 17-19 would be allowable if rewritten to overcome the rejections under 35 U.S.C 112(b), set forth in this Office Action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The closest prior art is as follows:
Hombroek et al (Publication No. 20220195652) discloses a laundry washing machine (Paragraph 0036, 0038) using a controller (Paragraph 0058) for analyzing, using a machine learning model, an image of a laundry load to identify a load characteristic (Paragraph 0141). As well as determining the weight of the load.
Beveal et al (Patent No. 10,466,665) discloses the use of a laundry machine by multiple users and splitting the payment between the multiple users (Col 25 lines 11-21) and ability to alter pricing depending on multiple factors (Col 29 lines 4-10).
Carobene et al (EP 3751454 A1) discloses determining weight and size of laundry loads, as well as determining the color characteristics of the load.
Liz Stinson discloses connecting unique users to do laundry via an app and using the app for user confirmation and payment.
Cha et al (Publication No. US 20210062397) discloses the use of different machine learning models for image recognition.
However, when the closest prior art of record is taken in combination, they fail to teach or suggest the invention of claim 1 and 11. At best the references of record would provide a laundry appliance where the color and weight of individual laundry loads can be determines as well determining the cost of that load. The reference of record would not provide the laundry of two or more unique users within the same wash tub/basket.
Accordingly the reference of record does not teach all the limitations of the claims: A laundry treatment appliance defining a vertical direction, a lateral direction, and a transverse direction, the laundry treatment appliance comprising:
a cabinet defining a receiving space;
a wash tub provided within the receiving space;
a wash basket received within the wash tub; and
a controller provided within the cabinet, the controller being configured to direct a laundry operation, the laundry operation comprising:
receiving a plurality of wash requests from a plurality of unique users, wherein each wash request of the plurality of wash requests comprises an image of a unique laundry load;
analyzing, by one or more computing devices using a machine learning image recognition model, a first image to evaluate one or more laundry load characteristics of a first unique laundry load;
analyzing, by the one or more computing devices using the machine learning image recognition model, a second image to evaluate one or more laundry load characteristics of a second unique laundry load;
matching a first unique user and a second unique user from the plurality of users, the first user and the second user having a matching laundry load characteristic from the one or more laundry load characteristics; and
generating a joint washing cycle subsequent to matching the first and second unique users based on the matching laundry load characteristics.
Conclusion


NEXT 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 .
Applicant’s response to the Non-final Office Action dated 04/14/2025, filed with the office on 05/20/2025, has been entered and made of record.
Response to Amendment
In light of Applicant’s amendments, Applicant’s remarks presented in Pages 10-11 of its Reply has been found to be persuasive.  Accordingly, the rejection of record of claims under 35 U.S.C. 101 has been withdrawn.
In light of Applicant’s amendment of the claims 4-5, 8-10, 13-14, and 17-19, the rejections of record under 35 U.S.C. 112(b) for the claims including insufficient antecedent basis have been withdrawn.
Allowable Subject Matter
Claims 1-19 are allowed.  As states in the None-final Office Action, the following is the examiner stated reasons for indication of allowable subject matter:
Hombroek et al (Publication No. US 2022/0195652) discloses a laundry washing machine (Paragraph 0036, 0038) using a controller (Paragraph 0058) for analyzing, using a machine learning model, an image of a laundry load to identify a load characteristic (Paragraph 0141). As well as determining the weight of the load.
Beveal et al (US Patent No. 10,466,665) discloses the use of a laundry machine by multiple users and splitting the payment between the multiple users (Col 25 lines 11-21) and ability to alter pricing depending on multiple factors (Col 29 lines 4-10).
Carobene et al (EP 3751454 A1) discloses determining weight and size of laundry loads, as well as determining the color characteristics of the load.
Liz Stinson discloses connecting unique users to do laundry via an app and using the app for user confirmation and payment.
Cha et al (Publication No. US 2021/0062397) discloses the use of different machine learning models for image recognition.
However, when the closest prior art of record is taken in combination, they fail to teach or suggest the invention of claim 1 and 11. At best, the references of record would provide a laundry appliance where the color and weight of individual laundry loads can be determines as well determining the cost of that load. The reference of record would not provide the laundry of two or more unique users within the same wash tub/basket.
Accordingly the references of record do not teach the ordered combination of the limitations of the independent claims: “A laundry treatment appliance defining a vertical direction, a lateral direction, and a transverse direction, the laundry treatment appliance comprising: a cabinet defining a receiving space; a wash tub provided within the receiving space; a wash basket received within the wash tub; and a controller provided within the cabinet, the controller being configured to direct a laundry operation, the laundry operation comprising: receiving a plurality of wash requests from a plurality of external devices associated with a plurality of unique users, wherein each wash request of the plurality of wash requests comprises an image of a unique laundry load and one or more wash parameters corresponding to the unique laundry load; analyzing, by one or more computing devices using a machine learning image recognition model, a first image to evaluate one or more laundry load characteristics of a first unique laundry load; analyzing, by the one or more computing devices using the machine learning image recognition model, a second image to evaluate one or more laundry load characteristics of a second unique laundry load; matching a first unique user and a second unique user from the plurality of unique users, the first unique user and the second unique user having a matching laundry load characteristic from the one or more laundry load characteristics; generating a joint washing cycle comprising one or more wash parameters corresponding to the unique laundry load subsequent to matching the first and second unique users based on the matching laundry load characteristics; and initiating the joint washing cycle at the laundry treatment appliance according to the one or more wash parameters.”
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.”
Conclusion

CITED REFERENCE SUMMARIES: Ref US20220195652A1: Claims:
A laundry washing machine, comprising: a wash tub disposed within a housing; a color detection sensor positioned to capture color composition data of a load of articles as the load of articles is added to the wash tub; a weight sensor operatively coupled to the wash tub to sense a weight of the load of articles as the load of articles is added to the wash tub; and a controller coupled to the color detection sensor and the weight sensor and configured to initiate a plurality of color composition data captures with the color detection sensor responsive to detected weight changes sensed by the weight sensor as the load of articles is added to the wash tub, the controller configured to initiate each color composition data capture in response at least in part to the weight sensor detecting a stable weight in the wash tub for at least a predetermined duration; wherein the controller is further configured to set one or more operational settings for a wash cycle based upon the plurality of color composition data captures.
a wash tub disposed within a housing;
a color detection sensor positioned to capture color composition data of a load of articles as the load of articles is added to the wash tub;
a weight sensor operatively coupled to the wash tub to sense a weight of the load of articles as the load of articles is added to the wash tub; and
a controller coupled to the color detection sensor and the weight sensor and configured to initiate a plurality of color composition data captures with the color detection sensor responsive to detected weight changes sensed by the weight sensor as the load of articles is added to the wash tub, the controller configured to initiate each color composition data capture in response at least in part to the weight sensor detecting a stable weight in the wash tub for at least a predetermined duration;
wherein the controller is further configured to set one or more operational settings for a wash cycle based upon the plurality of color composition data captures.
The laundry washing machine of claim 1 , wherein the controller is configured to detect the stable weight for at least the predetermined duration by determining that a weight change sensed by the weight sensor during the predetermined duration is less than a stability weight constant.
The laundry washing machine of claim 2 , wherein the controller is configured to detect the stable weight for at least the predetermined duration by starting a stability timer when a weight change subsequent to a prior composition data capture is greater than an article addition weight constant.
The laundry washing machine of claim 3 , wherein the controller is configured to store a stability weight in connection with starting the stability timer and reset the stability timer if and the stability weight if the weight change sensed by the weight sensor during the predetermined duration exceeds the stability weight constant.
The laundry washing machine of claim 4 , wherein the controller is configured to initiate a color composition data capture in response to the stability timer reaching the predetermined duration.
The laundry washing machine of claim 1 , wherein the controller is further configured to detect removal of an article from the wash tub using the weight sensor and delete a color composition data capture in response to detecting removal of the article.
The laundry washing machine of claim 1 , wherein the controller is further configured to initiate each color composition data capture in response at least in part to detection of image stability in the wash tub.
The laundry washing machine of claim 1 , wherein the controller is configured to initiate the plurality of color composition data captures in response to detecting an open door, and to determine that all articles in the load have been added to the wash tub in response to detecting a closed door.
The laundry washing machine of claim 8 , wherein the controller is configured to initiate an additional capture of color composition data after detecting the closed door, and to delete the additional capture of color composition data if a weight change sensed by the weight sensor is less than a predetermined constant.
The laundry washing machine of claim 8 , wherein the controller is further configured to initiate a capture of an additional color composition data capture after detecting the closed door in response to detecting that the door has been reopened and that the weight sensor has detected another article added to the wash tub.
The laundry washing machine of claim 8 , wherein the controller is configured to start a sleep timer in response to detecting the open door, to reset the sleep timer in connection with each color composition data capture, to put the laundry washing machine in a sleep state in response to expiration of the sleep timer, and to awaken the laundry washing machine and initiate a color composition data capture in response to detection of a weight change by the weight sensor while the laundry washing machine is in the sleep state.
The laundry washing machine of claim 1 , wherein the color detection sensor comprises an image sensor configured to capture a digital image, and wherein the laundry washing machine further comprises a light positioned to illuminate the wash tub during image capture with the image sensor.
The laundry washing machine of claim 1 , wherein the controller is further configured to initiate a color decision algorithm to characterize the captured color composition data by assigning each of a plurality of pixels in the captured color composition data to one of a plurality of color categories, and to characterize the load of articles based upon the characterized color compensation data, and to set one or more operational settings for a wash cycle based upon the characterization of the load of articles by the color decision algorithm.
A laundry washing machine, comprising: a wash tub disposed within a housing; a color detection sensor positioned to capture color composition data of a load of articles as the load of articles is added to the wash tub; and a controller coupled to the color detection sensor and configured to initiate the capture of the color composition data with the color detection sensor as the load of articles is added to the wash tub, the controller further configured to initiate a color decision algorithm to characterize the captured color composition data by assigning each of a plurality of pixels in the captured color composition data to one of a plurality of color categories, and to characterize the load of articles based upon the characterized color compensation data; wherein the controller is further configured to set one or more operational settings for a wash cycle based upon the characterization of the load of articles by the color decision algorithm.
a color detection sensor positioned to capture color composition data of a load of articles as the load of articles is added to the wash tub; and
a controller coupled to the color detection sensor and configured to initiate the capture of the color composition data with the color detection sensor as the load of articles is added to the wash tub, the controller further configured to initiate a color decision algorithm to characterize the captured color composition data by assigning each of a plurality of pixels in the captured color composition data to one of a plurality of color categories, and to characterize the load of articles based upon the characterized color compensation data;
wherein the controller is further configured to set one or more operational settings for a wash cycle based upon the characterization of the load of articles by the color decision algorithm.
The laundry washing machine of claim 14 , wherein the controller is configured to initiate the color decision algorithm by executing at least a portion of the color decision algorithm.
The laundry washing machine of claim 14 , wherein the controller is configured to initiate the color decision algorithm by communicating data from the plurality of color composition data captures to a remote device that executes at portion of the color decision algorithm, and wherein the controller is further configured to receive result data associated with the color decision algorithm from the remote device.
The laundry washing machine of claim 14 , wherein the color decision algorithm is configured to assign each of the plurality of pixels in the captured color composition data to one of the plurality of color categories by comparing color data for each pixel against a plurality of thresholds associated with the plurality of color categories.
The laundry washing machine of claim 14 , wherein the color decision algorithm is configured to determine a number of pixels in the captured color composition data that are assigned to each of the plurality of color categories, and to characterize the load of articles based upon the determined numbers of pixels.
The laundry washing machine of claim 18 , wherein the color decision algorithm is configured to characterize the load of articles based upon the determined numbers of pixels by comparing the determined numbers of pixels against thresholds associated with one or more of the plurality of color categories.
The laundry washing machine of claim 19 , wherein the plurality of color categories to which the pixels are assigned includes a whites color category, a lights color category, and a darks color category, and wherein the color decision algorithm is configured to characterize the load of articles based upon the determined numbers of pixels by: characterizing the load of articles as a whites load in response to a majority of the determined numbers of pixels being assigned to the whites color category and the determined number of pixels assigned to the whites color category meeting a whites threshold; characterizing the load of articles as a lights load in response to a majority of the determined numbers of pixels being assigned to the lights color category and the determined number of pixels assigned to the lights color category meeting a lights threshold; and characterizing the load of articles as a darks load in response to a majority of the determined numbers of pixels being assigned to the darks color category and the determined number of pixels assigned to the darks color category meeting a darks threshold.
characterizing the load of articles as a whites load in response to a majority of the determined numbers of pixels being assigned to the whites color category and the determined number of pixels assigned to the whites color category meeting a whites threshold;
characterizing the load of articles as a lights load in response to a majority of the determined numbers of pixels being assigned to the lights color category and the determined number of pixels assigned to the lights color category meeting a lights threshold; and
characterizing the load of articles as a darks load in response to a majority of the determined numbers of pixels being assigned to the darks color category and the determined number of pixels assigned to the darks color category meeting a darks threshold.
The laundry washing machine of claim 20 , wherein the plurality of color categories further includes a reds color category, and wherein the color decision algorithm is further configured to characterize the load of articles as a reds load in response to the determined number of pixels assigned to the reds color category meeting a reds threshold.
A method of operating a laundry washing machine, the method comprising: capturing color composition data of a load of articles as the load of articles is added to a wash tub using a color detection sensor; sensing a weight of the load of articles as the load of articles is added to the wash tub using a weight sensor operatively coupled to the wash tub; initiating a plurality of color composition data captures with the color detection sensor responsive to detected weight changes sensed by the weight sensor as the load of articles is added to the wash tub, wherein each color composition data capture is initiated in response to the weight sensor detecting a stable weight in the wash tub for at least a predetermined duration; initiating a color decision algorithm to characterize the captured color composition data by assigning each of a plurality of pixels in the captured color composition data to one of a plurality of color categories, and to characterize the load of articles based upon the characterized color compensation data; and setting one or more operational settings for a wash cycle based upon the plurality of color composition data captures.
capturing color composition data of a load of articles as the load of articles is added to a wash tub using a color detection sensor;
sensing a weight of the load of articles as the load of articles is added to the wash tub using a weight sensor operatively coupled to the wash tub;
initiating a plurality of color composition data captures with the color detection sensor responsive to detected weight changes sensed by the weight sensor as the load of articles is added to the wash tub, wherein each color composition data capture is initiated in response to the weight sensor detecting a stable weight in the wash tub for at least a predetermined duration;
initiating a color decision algorithm to characterize the captured color composition data by assigning each of a plurality of pixels in the captured color composition data to one of a plurality of color categories, and to characterize the load of articles based upon the characterized color compensation data; and
setting one or more operational settings for a wash cycle based upon the plurality of color composition data captures.
Ref US10466665B2: Claims:
A system comprising: at least one processor; and a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed, instruct the at least one processor to perform operations comprising: receiving a request for use of a laundry machine that is one of multiple laundry machines in a vicinity of a mobile computing device, the request sent by the mobile computing device responsive to a determination that the mobile computing device is in proximity to the laundry machine, wherein a wireless network interface of the mobile computing device receives a beacon signal that is emitted from each of the multiple laundry machines, wherein the determination that the mobile computing device is in proximity to the laundry machine is based on: i) a received signal level of the beacon signal from the laundry machine having at least a strength that corresponds to a threshold distance between the mobile computing device and the laundry machine, and ii) the received signal level of the beacon signal from the laundry machine being stronger than a respective signal level of each of the other beacon signals from the other laundry machines in the vicinity, wherein the beacon signal includes an identifier of the laundry machine that uniquely identifies the laundry machine among the multiple laundry machines, wherein the identifier included in the beacon signal changes periodically, and wherein the identifier is included in the request, sent by the mobile computing device, for use of the laundry machine; determining a network address of the laundry machine indicated in the request; and sending a control signal to the network address of the laundry machine, wherein the control signal causes an altering of an operational state of the laundry machine to enable use of the laundry machine during a period of time.
at least one processor; and
a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed, instruct the at least one processor to perform operations comprising: receiving a request for use of a laundry machine that is one of multiple laundry machines in a vicinity of a mobile computing device, the request sent by the mobile computing device responsive to a determination that the mobile computing device is in proximity to the laundry machine, wherein a wireless network interface of the mobile computing device receives a beacon signal that is emitted from each of the multiple laundry machines, wherein the determination that the mobile computing device is in proximity to the laundry machine is based on: i) a received signal level of the beacon signal from the laundry machine having at least a strength that corresponds to a threshold distance between the mobile computing device and the laundry machine, and ii) the received signal level of the beacon signal from the laundry machine being stronger than a respective signal level of each of the other beacon signals from the other laundry machines in the vicinity, wherein the beacon signal includes an identifier of the laundry machine that uniquely identifies the laundry machine among the multiple laundry machines, wherein the identifier included in the beacon signal changes periodically, and wherein the identifier is included in the request, sent by the mobile computing device, for use of the laundry machine; determining a network address of the laundry machine indicated in the request; and sending a control signal to the network address of the laundry machine, wherein the control signal causes an altering of an operational state of the laundry machine to enable use of the laundry machine during a period of time.
receiving a request for use of a laundry machine that is one of multiple laundry machines in a vicinity of a mobile computing device, the request sent by the mobile computing device responsive to a determination that the mobile computing device is in proximity to the laundry machine, wherein a wireless network interface of the mobile computing device receives a beacon signal that is emitted from each of the multiple laundry machines, wherein the determination that the mobile computing device is in proximity to the laundry machine is based on: i) a received signal level of the beacon signal from the laundry machine having at least a strength that corresponds to a threshold distance between the mobile computing device and the laundry machine, and ii) the received signal level of the beacon signal from the laundry machine being stronger than a respective signal level of each of the other beacon signals from the other laundry machines in the vicinity, wherein the beacon signal includes an identifier of the laundry machine that uniquely identifies the laundry machine among the multiple laundry machines, wherein the identifier included in the beacon signal changes periodically, and wherein the identifier is included in the request, sent by the mobile computing device, for use of the laundry machine;
determining a network address of the laundry machine indicated in the request; and
sending a control signal to the network address of the laundry machine, wherein the control signal causes an altering of an operational state of the laundry machine to enable use of the laundry machine during a period of time.
The system of claim 1 , wherein the threshold distance is previously determined based on a calibration to measure distance versus signal level for a transmitted power of the beacon signal.
The system of claim 1 , wherein the request includes a code that identifies the laundry machine and that is presented on or in proximity to the laundry machine, and wherein the code is presented in a scannable barcode of at least one dimension.
The system of claim 1 , the operations further comprising: receiving an availability request for availability information describing currently available laundry machines, the availability request indicating a location of one or more of a user or the mobile computing device; in response to the availability request, identifying one or more laundromats withi
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