DETAILED ACTION
Status of the Claims
Original claims 1-20 filed August 13, 2024, are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 13, 2024, is being considered by the examiner.
Claim Objections
Claim(s) 9 is/are objected to because of the following informalities:
In claim 9, line 2, “a subset” should be “the subset”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “determining, at the processor of the mobile device, a loading pattern of at least a subset of dishes of the set of dishes based on at least one dish parameter of the subset of dishes and at least one rack parameter of the one or more racks.” This limitation is computer-implemented at least because it is performed at a processor of a mobile device. This limitation is functional at least because it recites a function of determining a loading pattern.
MPEP 2161.01, Subsection I, provides instructions for determining whether there is adequate written description for a computer-implemented functional claim limitation, including the following:
“[O]riginal claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.”
“When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340, 86 USPQ2d 1609, 1623 (Fed. Cir. 2008) (internal citation omitted). It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding "whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved"). If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made.”
Examiner has reviewed the specification in search of an algorithm for performing the “determining … a loading pattern …” limitation of claim 1 and finds the following portion to be most relevant (unless otherwise noted, all references are to the specification as published in US 2026/0047742 A1):
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This portion of the specification generally restates the function recited in the claim, but does not describe any algorithm (i.e., finite sequence of steps) for determining a loading pattern or otherwise explain how the inventor intends for such a function to be performed. Inputs to and outputs from the processor are identified, but there is no explanation of what steps the processor takes to transform the inputs into the outputs.
The noted portion of the specification does state that the processor may include a neural network, generative AI, machine learning, etc. However, the specification does not specifically state that any of these broad categories of tools are used to determine the loading pattern, nor does it describe any algorithm for using one of these categories of tools to determine the loading pattern or otherwise explain specifically how the inventor may intend to use one of these categories of tools for determining a loading pattern. A general statement that a broad class of machine learning tools may be present in a processor does not describe how to determine a loading pattern of at least a subset of dishes based on at least one dish parameter and at least one rack parameter, as required by the claimed invention.
Given this lack of detail, one of ordinary skill in the art could not reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. Therefore, claim 1 fails to comply with the written description requirement under 35 U.S.C. 112(a).
Claims 2-20 include the limitations of claim 1 and therefore also fail to comply with the written description requirement for at least the same reasons as claim 1.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The patent Subject Matter Eligibility (SME) test is described in MPEP 2106. It includes multiple steps, sub-steps, and prongs.
Step 1
Claims 1-20 are to a process, so they fall within a statutory category.
Step 2A, Prong One
The claims recite an abstract idea. Specifically, the claims recite a mental process of deciding how to load certain dishes into a dishwasher. Mental process abstract ideas are discussed at MPEP 2106.04(a)(2), Subsection III.
Claim 1 is a representative claim. Claim 1 recites “capturing, with the imager of the mobile device, an image of a set of dishes”. This limitation falls within the mental processes grouping of abstract ideas at least because a human can look at a set of dishes and visually perceive them (i.e., capture a mental image of the dishes). Examiner notes that while the recitation of an “imager” and a “mobile device” may require use of a computer, this does not preclude the claim from reciting a mental process. Id. at further subsection C. Claim 1 further recites “determining, at the processor of the mobile device, a loading pattern of at least a subset of dishes of the set of dishes based on at least one dish parameter of the subset of dishes and at least one rack parameter of the one or more racks”. This limitation also falls within the mental processes grouping of abstract ideas at least because a human can mentally decide where to place certain dishes within a dishwasher given various parameters, such as sizes of the dishes and spaces within a rack of the dishwasher. For example, a human can mentally determine to place a plate of a certain size between tines of a dishwasher rack that are spaced far enough apart to fit the plate. Again, requirement that the determination is made “at the processor” is not sufficient to preclude recitation of a mental process – see above. Claim 1 further recites “providing, at the user interface, the loading pattern as an image comprising the at least one or more racks and the subset of dishes.” This limitation also falls within the mental processes grouping of abstract ideas at least because a human can mentally provide a loading pattern as an image comprising racks and dishes. For example, the human could use pen and paper to draw a diagram showing the rack and positions where each of the dishes should be placed. Note that the use of a physical aid such as pen and paper to help perform a mental step does not negate the mental nature of the limitation. Id. at further subsection B.
The method of claim 1 is similar to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, as was recited in the claims at issue in Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), which is an example of a mental process abstract idea. Id. at further subsection A. The capturing of an image and use of dish and rack parameters in claim 1 is information collection, the determination of a loading pattern in claim 1 is analysis of the collected information, and the provision of the loading pattern in claim 1 is displaying certain results of the collection and analysis. The data analysis step of claim 1 (i.e., the determining a loading pattern) is recited at such a high level of generality that it can be practically performed in the human mind – see explanation above.
Claims 2-4 recite determining further parameters and such parameter determination can be performed mentally.
Claims 5 and 13 further recite image capturing that can be performed mentally for substantially the same reasons discussed above.
Claim 6 recites refining or expanding the subset of dishes based on user feedback, which can be performed mentally by, for example, considering which dishes another person (i.e., a user) requests to be loaded into the dishwasher.
Claim 7 recites providing the subset of dishes within a selection box, which could be performed mentally using various physical aids. In one example, a human could place a subset of dishes to be loaded into a dishwasher into a physical box. In another example, a user could write a list of dishes in the subset to be loaded into the dishwasher within a box drawn on a piece of paper.
Claims 8 and 9 recite locations/components where the pattern is determined and claim 10 requires a processor comprising a neural network, machine learning, or generative Artificial Intelligence but, as discussed above, even claims that require a computer may still recite a mental process.
Claims 11 and 12 require providing specific formats of images – i.e., images with the racks loaded and/or 3D images – and these formats can be provided mentally (e.g., by drawing a perspective view of the rack with the dishes loaded according to the determined loading pattern).
Claim 13 recites determining an optimization suggestion based on images and this may be performed mentally. For example, a person may view a loaded dishwasher and mentally recognize/determine that a dish is misplaced and should be moved to improve/optimize washing. For example, a person could recognize that a bowl facing upwards and away from washing nozzles should be rotated to face downwards in order to optimize the dishwasher’s ability to wash it.
Claim 14 recites that the image is processed through the processor but, again, even claims that require a computer may still recite a mental process.
Claims 15 and 16 recite displaying warnings, which can be performed mentally such as by writing them down as messages on a piece of paper.
Claims 17-20 further recite considering additional user input such as a maximum load size or a maximum cleanliness level and such consideration of additional input can be performed mentally for substantially the same reasons discussed above.
Claim 19 further recites determining a pattern configured to fit each dish and claim 20 further recites optimizing cleaning coverage, which can both be performed mentally. Humans can mentally perform a task with certain goals in mind. Within the context of loading a dishwasher, a human can mentally decide how to place dishes in a manner that meets goals of fitting all dishes while optimizing cleaning coverage (e.g., by ensuring all dishes are placed within the dishwasher, while providing increased space around dirtier dishes, and so on).
Step 2A, Prong Two
The claims do not recite additional elements that integrate the mental process into a practical application. As described above, many of the claim limitations are directed to the mental process itself and therefore are not additional elements. Of the additional elements that are recited, none go beyond:
Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
Claim 1 recites “using a mobile device having an imager, a user interface, and a processor”. Claim 1 and the dependent claims further recite using the mobile device, and various components thereof, to perform certain steps. Claim 10 specifies that the processor comprises machine learning. However, these limitations only amount to mere instructions to implement an abstract idea or other exception on a computer, which does not integrate an abstract idea into a practical application. For example, while the claims require certain steps to be performed by a processor, the claims generally recite only the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished. To illustrate, claim 1 requires determining a loading pattern at the processor, but does not explain how that loading pattern is determined. In another example, the claims invoke computers or other machinery merely as a tool to perform an existing process. Humans determine how to load dishes into dishwashers every day and have for some time. The claims use mobile device computer machinery merely as a tool to perform this existing process.
The preamble of claim 1 recites that the method is for use with a “dishwasher having a tub at least partially defining a treating chamber, with an access opening, at least one or more racks positioned within the treating chamber.” Various other claim limitations pertain to specific parameters of dishes and/or dishwasher racks. This is merely an attempt to limit the use of the abstract idea to a particular dishwasher technological environment, similar to how limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid was found to be simply an attempt to limit the use of the abstract idea to a particular technological environment – see MPEP 2106.05(h), example vi, citing to Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
Step 2B
The identification of additional elements and conclusions from Step 2A, Prong Two are carried over to Step 2B. MPEP 2106.05, Subsection II. No additional elements were considered to be insignificant extra-solution activity. No additional elements, or combinations of elements go beyond simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. As discussed above, the additional elements amount to merely including instructions to implement an abstract idea on a computer or linking the use of a judicial exception to a particular technological environment or field of use, neither of which amounts to significantly more than an abstract idea.
Conclusion
Claims 1-20 are patent-ineligible under 35 U.S.C. 101 because they are directed to an abstract idea and do not recite additional elements that amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-9, 11-12, and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by ‘Deng’ (CN 114968016 A)1.
Regarding claim 1, Deng discloses a method of loading dishes within a dishwasher (e.g., Figure 1) using a mobile device (e.g., [0033]-[0036], Fig. 2, Fig. 12, mobile terminal) having an imager (e.g., [0036], [0042], Fig. 2, camera; Also see [0109], Fig. 12, user interface 1203 including a camera), a user interface (e.g., [0036], [0044], [0056] et seq., display of mobile terminal provides a user interface; Also see [0109], Fig. 12, user interface 1203), and a processor (e.g., [0107], [0113], Fig. 12, processor 1201), the dishwasher having a tub at least partially defining a treating chamber, with an access opening, at least one or more racks positioned within the treating chamber (e.g., Figs. 9 and 10 illustrate a frontal view of the dishwasher looking through an access opening to racks positioned within a tub at least partially defining a treating chamber; Also see, e.g., [0055], for further discussion of the racks), the method comprising:
capturing, with the imager of the mobile device, an image of a set of dishes (e.g., [0041]-[0043], Fig. 1, S101, and Fig. 2, image of set of dishes is captured to determine size information of tableware; e.g., [0046]-[0051], Fig. 1, S101, and Figs. 5-7, a second image-based tableware size measurement and counting technique is described; Either falls within the scope of the claimed image capture);
determining, at the processor of the mobile device (e.g., [0113], processor 1201 of mobile device executes the method), a loading pattern of at least a subset of dishes of the set of dishes ([0059] et seq., Fig. 1, S104, position information of each piece of tableware – i.e., a loading pattern – is determined for the at least subset of dishes selected for washing, such as the 13 pieces of tableware identified in the example illustrated in Fig. 7) based on at least one dish parameter of the subset of dishes (e.g., [0060], size parameters of each dish) and at least one rack parameter of the one or more racks (e.g., [0060], [0055], size parameters of dish rack); and
providing, at the user interface, the loading pattern as an image comprising the at least one or more racks and the subset of dishes (e.g., [0068]-0071], Fig. 1, S105, and Figs. 9-10, user interface displays a loading pattern image to the user).
Regarding claim 2, Deng discloses the method of claim 1, wherein the determining the loading pattern further comprises determining a dishwasher parameter (e.g., [0055], dishwasher model identifier).
Regarding claim 3, Deng discloses the method of claim 2, wherein the at least one rack parameter includes one or more of a location of a sprayer, a layout of tines, or a height of the one or more racks (e.g., [0055], height of dish rack).
Regarding claim 4, Deng discloses the method of claim 1, wherein the at least one dish parameter includes one or more of a size of a dish (Fig. 1, S101, [0039] et seq., sizes of tableware dishes are determined), a dish type, or a material of a dish.
Regarding claim 5, Deng discloses the method of claim 1, wherein capturing, with the imager of the mobile device, the image of the set of dishes includes imaging the set of dishes outside of the dishwasher (e.g., Figs. 2-3 and 6-7 show dish images being captured on a flat surface outside of the dishwasher; e.g., Fig. 1, [0071], images are captured to identify size at S101 before loading pattern image is provided S105, and all of this is performed before the user places the tableware dishes into the dishwasher).
Regarding claim 6, Deng discloses the method of claim 5, further comprising one of refining or expanding the subset of dishes based on user feedback (e.g., [0042]-[0043], user can capture more images of more dishes to expand the subset of dishes to be washed; e.g., [0044], user can manually select pieces of tableware to wash).
Regarding claim 7, Deng discloses the method of claim 5, wherein refining or expanding the subset of dishes includes providing the subset of dishes within a selection box (e.g., [0043], Fig. 3, upload button selection box, thumbnail image box, etc.; e.g., [0042], Fig. 2, frame of camera forms a selection box, with dishes within the frame/selection box being selected for washing).
Regarding claim 8, Deng discloses the method of claim 1, wherein determining the loading pattern comprises determining, at the user interface, the subset of dishes of the set of dishes (e.g., [0044], user interface is used to select subset of dishes to be washed).
Regarding claim 9, Deng discloses the method of claim 1, wherein determining the loading pattern comprises determining, at the processor, a subset of dishes of the set of dishes (e.g., [0051], Fig. 7, a subset of 13 pieces of tableware is determined for washing; e.g., [0113], processor 1201 of mobile device executes the method of Fig. 1).
Regarding claim 11, Deng discloses the method of claim 1, wherein the providing, at the user interface, the loading pattern as an image comprising the at least one or more racks and the subset of dishes comprises displaying an image or an interactive image of the at least one or more racks loaded with the subset of dishes on the user interface ([0056]-[0058], Fig. 9, image of racks loaded with subset of dishes to be washed is displayed; The displayed image can be considered “interactive” at least because it is displayed on a user interface with which the user interacts).
Regarding claim 12, Deng discloses the method of claim 11, wherein the interactive image is a 3D model of the one or more racks ([0057], tableware icons and dish rack illustrations can be 3D images).
Regarding claim 17, Deng discloses the method of claim 1, wherein the determining the loading pattern of the subset of dishes is based on the at least one dish parameter of the subset of dishes (see rejection of claim 1), the at least one rack parameter of the at least one or more racks (see rejection of claim 1), and a user input (e.g., [0044], user inputs the dishes to be cleaned using selection commands; e.g., [0053], Fig. 8, user inputs number of family members to determine quantity of each tableware; e.g., [0063], [0066], loading pattern is determined based on the total number of dishes by calculating an average and/or iterating until the total number of dishes have been placed).
Regarding claim 18, Deng discloses the method of claim 17, wherein the user input includes a load size (e.g., [0044], [0053], user input includes load size as number of tableware items to be washed) or a cleanliness level.
Regarding claim 19, Deng discloses the method of claim 18, the load size includes a maximum load size (e.g., [0044], [0053], user input includes the number of tableware items that are to be washed; This can be considered a maximum load size because no other tableware items will be included in the loading pattern) and the loading pattern is configured to fit each dish of the subset of dishes (e.g., [0066], dishes are placed into loading pattern until all dishes in the subset to be washed are placed).
Regarding claim 20, Deng discloses the method of claim 18, wherein the cleanliness level is a maximum cleanliness level (Examiner notes that claim 20 depends from claim 18, which requires input of a load size or a cleanliness level; As explained above in the rejection of claim 18, Deng discloses a load size, so Deng satisfies the scope of claims 18 and 20 even if it does not disclose a [maximum] cleanliness level) and the loading pattern is configured to orient the subset of dishes for an optimized cleaning coverage (e.g., [0071], loading pattern avoids non-standard placement and enhances the washing effect of the dishwasher).
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.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng in view of ‘Shin’ (US 2021/0093150 A1).
Regarding claim 10, Deng teaches the method of claim 9.
Deng’s processor uses a template matching technique to recognize dishes in images (e.g., [0041]).
Deng does not explicitly each that its processor comprises a neural network, machine learning, or generative Artificial Intelligence.
However, Shin does teach using a neural network, machine learning, or generative Artificial Intelligence to recognize dishes in images (e.g., Figs. 9-10, [0254] et seq.).
Deng’s method differs from the claimed method by the substitution of Deng’s use of template matching for a neural network, machine learning, or generative Artificial Intelligence. Shin’s teachings demonstrate that dish detection neural networks, machine learning, or generative Artificial Intelligence and their functions were known in the prior art. As both Deng’s template matching and Shin’s neural network, machine learning, or generative Artificial Intelligence both recognize dishes, one of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable. For at least these reasons, it would have been obvious to one of ordinary skill in the art to simply substitute the neural network, machine learning, or generative Artificial Intelligence of Shin for the template matching of Deng to produce the method of claim 9. Therefore, claim 9 is obvious over Deng in view of Shin. See MPEP 2143, Subsection I.B.
Claim(s) 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng in view of ‘Uyeda’ (US 2023/0346192 A1).
Regarding claim 13, Deng teaches the method of claim 1.
Deng provides placement guidance showing positions where tableware should be placed in a dishwasher that users can refer to while loading a dishwasher (e.g., [0071]). Deng apparently assumes a user will place the dishes correctly. Deng does not further teach:
capturing one or more images of the subset of dishes after the loading into the one or more racks,
determining an optimization suggestion based on the one or more images of the subset of dishes after the loading into the one or more racks, and
providing the optimization suggestion.
However, Uyeda does teach a technique for verifying that users have placed dishes into a dishwasher correctly (e.g., Fig. 4) by:
capturing one or more images of the subset of dishes after the loading into the one or more racks (e.g., [0053], Fig. 4, step 402; Fig. 5 shows an example),
determining an optimization suggestion based on the one or more images of the subset of dishes after the loading into the one or more racks (e.g., [0053] et seq., Fig. 4, step 404, image is analyzed to identify uncleanable dishes and open spaces to which they should be moved), and
providing the optimization suggestion (e.g., [0067] et seq., Fig. 4, steps 406-408, optimization suggestion to move uncleanable dish to an open space is provided via display; Fig. 7 shows an example).
As discussed above, Deng apparently assumes that a user will load a dishwasher correctly by following the provided layout pattern, but incorrect dish placement may still occur. For example, a user may make a mistake and not follow the provided layout pattern exactly. In another example, a user may place a dish in the correct position indicated in the provided layout pattern, but facing a wrong way (e.g., a bowl facing up instead of down). In another example, further dishes may be added to the dishwasher after initial layout-guided loading has already concluded and these further dishes may be improperly placed (e.g., on top of other dishes in an uncleanable configuration). Applying the further image verification of Uyeda would advantageously allow any of these errors to be detected and corrected before running the dishwasher, thereby ensuring that all dishes were adequately cleaned.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Deng with the further image verification of Uyeda in order to improve the method with the reasonable expectation that this would result in a method that could detect and guide correction of possible dish placement errors, thereby ensuring that all dishes were adequately cleaned by the dishwasher. This technique for improving the method of Deng was within the ordinary ability of one of ordinary skill in the art based on the teachings of Uyeda.
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Deng and Uyeda to obtain the invention as specified in claim 13.
Regarding claim 14, Deng in view of Uyeda teaches the method of claim 13, and Uyeda further teaches that the determining the optimization suggestion comprises processing the image of the subset of dishes after loading into the one or more racks through the processor (e.g., [0053], Fig. 3, image analysis at step 404 may be performed in controller 270 of mobile device 210).
Regarding claim 15, Deng in view of Uyeda teaches the method of claim 13, and Uyeda further teaches that the providing the optimization suggestion comprises displaying one or more warnings on the user interface (e.g., Fig. 7, warning text in element 610).
Regarding claim 16, Deng in view of Uyeda teaches the method of claim 15, and Uyeda further teaches that the one or more warnings are indicative of a mis-placed dish of the subset of dishes (e.g., Fig. 7, warning text in element 610 is indicative of a dish being mis-placed in an overlapping, uncleanable position).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
‘Imai’ (“A User Support System That Optimizes Dishwasher Loading,” 2017)
Describes a system that receives an image of dishes on a table, recognizes and counts the types of dishes in the image, determines an optimal loading layout for the dishes in a dishwasher, and displays the optimal layout to a user – e.g., Fig. 1
‘Maeda’ (JP 2017-137195 A)
Shares common inventors/authors with the Imai reference and has a similar, but not identical, disclosure
‘Johnson’ (“Machine Vision Sorting and Inspection in Commercial Automatic Dishwashing,” 1993)
An early example of image-based dish type recognition and sorting/arrangement
‘Urata’ (JP 2023-10095 A)
Uses an image to identify tableware and recommend a dishwasher arrangement – e.g., Figs. 6-7
‘Kim’ (US 2025/0176794 A1)
Recognizes dishes in an image and guides a robot to place them in a dishwasher – e.g., Figs. 3-4
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEOFFREY E SUMMERS whose telephone number is (571)272-9915. The examiner can normally be reached Monday-Friday, 7:00 AM to 3:30 PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at (571) 272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GEOFFREY E SUMMERS/Examiner, Art Unit 2669
1 This reference was cited in the IDS filed August 13, 2024. The copy provided with the IDS does not include an English translation. A copy of the reference with an appended English translation is attached to this Office Action.
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