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 .
The Office acknowledges the amendment dated 20 November 2025, in which:
Claims 1-19 are currently pending.
Claims 1, 9, 14 and 17 are amended.
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.
The rejection to claims 1-8 under 35 U.S.C. § 101 is withdrawn in view of Applicant’s amendment.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 and 8-19 are rejected under 35 U.S.C. 103 as being unpatentable over Merrell et al. (US 2013/0222393, hereinafter “Merrell”) in view of Hudson et al. (US 2023/0153478, hereinafter “Hudson”).
With respect to Claim 1 (Currently Amended), Merrell teaches a computer-implemented method of producing an interior design for a room (Abstract, method/system for interactive layout), comprising:
receiving a selection of a room type at a computer terminal (Abstract, user specifies a room shape and furniture set (e.g., for a living room));
receiving, at the computer terminal, a floor plan defining the selected room type (Abstract, the user begins by specifying the shape of a room);
receiving a selection of one or more preferences related to the room type at the computer terminal (Abstract, user specifies “the set of furniture that must be arranged”);
receiving, at the computer terminal, a measurement for at least one wall defining the floor plan (Para. [0059], Fig. 7A, “room is created as a rectangle where its dimensions are specified”);
executing the floor plan algorithm to calculate any remaining wall dimensions associated with the floor plan and displaying the wall dimensions on the computer terminal (Para. [0059], Fig. 2A, step 252);
receiving, at the computer terminal, an input to adjust the wall dimensions (Abstract, the user “interactively… move any piece of furniture to modify the layout”, implying dimensional adjustments);
displaying, at the computer terminal, a plurality of additional elements as floor plan limitations addable to the floor plan by dragging and dropping (Para. [0059], features of a room can be specified such as the locations for doors, windows or other room features, such as areas where furniture cannot be placed), and
to identify at least one block that is functionally and dimensionally appropriate for the floor plan based on any of one or more user preferences and floor plan limitations (Fig. 2B, system suggest a set of furniture layouts that follow the interior design guidelines);
displaying at the computer terminal, at least one identified block as a design proposal (Abstract, Para. [0056], Fig. 2B, system suggests a set of furniture layouts);
receiving, at the computer terminal, a selection of a design proposal (Abstract, Para. [0013], user can interactively select a suggestion); and
automatically populating the floorplan, at the computer terminal, with the selected design proposal such that the at least one identified block as the design proposal is positioned in accordance with the calculated wall dimensions, clearance requirements, anchor points, one or more user preferences and floor plan limitations (Abstract, Claim 1, the selected layout is displayed).
Merrell fails to expressly disclose:
updating the floor plan dataset with the wall adjustment inputs and iteratively refining the floor plan algorithm to minimize future wall adjustment inputs;
executing the design proposal algorithm, the design proposal algorithm comprising a trained machine-learning model configured to analyze a database of design element blocks defined by dimensions, clearance requirements, anchor points, and configuration restrictions.
However, Hudson teaches
by one or more processors (Hudson: Para. [0122]) executing a floor plan algorithm and a design proposal algorithm stored in a memory device (Merrell, Para. [0109] – [0110], uses a floor plan algorithm (rectangles/dimensions) and a proposal algorithm (MCMC sampler). However, Hudson, at Para. [0043] – [0047], teaches enhancing these with a “design engineer” and “machine learning engine.”)
updating the floor plan dataset with the wall adjustment inputs and iteratively refining the floor plan algorithm to minimize future wall adjustment inputs (Hudson: Para. [0043], training data is used and “later refined to train the machine learning algorithm to dynamically perform the discloses operations.” Hudson also discloses, at Para. [0037], “issue avoidance” which minimizes future errors/inputs);
executing the design proposal algorithm, the design proposal algorithm comprising a trained machine-learning model configured to analyze a database of design element blocks defined by dimensions, clearance requirements, anchor points, and configuration restrictions (Hudson, Fig. 2, utilizes a machine learning engine 215 trained on items with “dimensions” (Para. [0045] – [0046]) and rules (Para. [0048], [0063]) regarding “how closely items are placed” (Clearance) and infrastructure locations (Anchors)).
Therefore, it would be obvious to one of ordinary skill in the art to modify the interactive layout system and method, as taught by Merrell, to incorporate the machine learning and data-driven refinement capabilities of Hudson, in order to intelligently propose designs and enhance the user’s interaction (Hudson: Para. [0002], [0034], [0035]).
With respect to Claim 2, the combination of Merrell as modified by Hudson teaches the computer-implemented method of claim 1, wherein receiving the floor plan comprises presenting, at the computer terminal, a display screen with a window for a sketch of a room shape and recording a sketch of the room shape input via a touchscreen device associated with the computer terminal (Para. [0048], Fig. 12).
With respect to Claim 3, the combination of Merrell as modified by Hudson teaches the computer-implemented method of claim 1, wherein receiving the floor plan comprises receiving, at the computer terminal, a digital blueprint of the floor plan defining the selected room type (Fig. 7A, 700).
With respect to Claim 4, the combination of Merrell as modified by Hudson teaches the computer-implemented method of claim 1, wherein the floor plan limitations include one or more of a fireplace, a window, shelving, cabinetry, a niche, and a wall opening (Para. [0059], Fig. 7A).
With respect to Claim 5, the combination of Merrell as modified by Hudson teaches the computer-implemented method of claim 1, wherein the block comprises a plurality of related design elements (Para. [0064], a “furniture layout” inherently comprises multiple related elements).
With respect to Claim 6, the combination of Merrell as modified by Hudson teaches the computer-implemented method of claim 1, further comprising receiving, at the computer terminal, an input to receive alternative design proposal selections and adjust any selected design proposal by dragging and dropping (Abstract, Para. [0013], “user can interactively select a suggestion and move any piece of furniture to modify the layout”).
With respect to Claim 8, the combination of Merrell as modified by Hudson teaches the computer-implemented method of claim 1, further comprising providing one or more style choices and displaying a mood board on the computer terminal based on the style choice (Para. [0062]).
With respect to Claim 9 (Currently Amended), Merrell teaches a computer system for producing an interior design for a room, comprising:
a memory device (Fig. 7A, global memory 704, shared memory 708);
a plurality of user computer terminals having touchscreen devices; and one or more processors operative to (Para. [0048], Fig. 12, client-server computing environment which inherently has these components) execute a floor plan algorithm and a design proposal algorithm stored in the memory device (Merrell, Para. [0109] – [0110], uses a floor plan algorithm (rectangles/dimensions) and a proposal algorithm (MCMC sampler) and to:
receive a selection of a room type at the plurality of user computer terminals (Abstract, user specifies a room shape and furniture set (e.g., for a living room));
receive a floor plan defining the selected room type at the plurality of user computer terminals (Para. [0059], Fig. 7A, “room is created as a rectangle where its dimensions are specified”);
receive a selection of one or more preferences related to the floor plan at each of the plurality of user computer terminals (Abstract, user specifies “the set of furniture that must be arranged”);
receive a measurement for at least one wall defining the floor plan at the plurality of user computer terminals (Para. [0059], Fig. 7A, “room is created as a rectangle where its dimensions are specified”);
calculate, by a floor plan algorithm, any remaining wall dimensions associated with the floor plan and presenting the wall dimensions on the computer terminal (Para. [0059], Fig. 2A, step 252);
receive wall adjustment inputs at the touchscreen devices of the plurality of user computer terminals to adjust the wall dimensions (Abstract, the user “interactively… move any piece of furniture to modify the layout”, implying dimensional adjustments);
display a plurality of additional elements as floor plan limitations addable to the room by dragging and dropping (Para. [0059], features of a room can be specified such as the locations for doors, windows or other room features, such as areas where furniture cannot be placed);
identify, by executing the design proposal algorithm, at least one design element comprising at least one appliance or article of furniture defined by its dimensions and any limiting characteristics as a block that is functionally and dimensionally appropriate for the floor plan based on one or more user preferences and floor plan limitations (Fig. 2B, system suggest a set of furniture layouts that follow the interior design guidelines);
display at least one identified block as a design proposal at the plurality of user terminals (Abstract, Para. [0056], Fig. 2B, system suggests a set of furniture layouts);
receive a selection of a design proposal at the plurality of user terminals (Abstract, Para. [0013], user can interactively select a suggestion); and
populate the floorplan with the selected design proposal in accordance with the one or more user preferences and floor plan limitations (Abstract, Claim 1, the selected layout is displayed).
Merrell fails to expressly disclose:
updating the floor plan dataset with the wall adjustment inputs and iteratively refining the floor plan algorithm to minimize future wall adjustment inputs.
However, Hudson teaches
executing a floor plan algorithm and a design proposal algorithm stored in a memory device (Merrell uses a floor plan algorithm (rectangles/dimensions) and a proposal algorithm (MCMC sampler). However, Hudson, at Para. [0043] – [0047], teaches enhancing these with a “design engineer” and “machine learning engine.”);
store the wall adjustment inputs in the memory device as updates to the floor plan dataset and iteratively refine, by the one or more processors, the floor plan algorithm using the floor plan dataset to reduce a number of wall adjustment inputs for subsequent floor plans (Hudson: Para. [0043], training data is used and “later refined to train the machine learning algorithm to dynamically perform the discloses operations.” Hudson also discloses, at Para. [0037], “issue avoidance” which minimizes future errors/inputs).
Therefore, it would be obvious to one of ordinary skill in the art to modify the interactive layout system and method, as taught by Merrell, to incorporate the machine learning and data-driven refinement capabilities of Hudson, in order to intelligently propose designs and enhance the user’s interaction (Hudson: Para. [0002], [0034], [0035]).
With respect to Claim 10, the combination of Merrell as modified by Hudson teaches the system of claim 9, wherein the one or more processors are configured further for operations comprising;
storing the wall adjustment inputs as a floor plan dataset on the computer memory device (Fig. 7A); and
refining the floor plan algorithm to one that minimizes wall adjustment inputs over the floor plan dataset (Para. [0105], [0126]).
With respect to Claim 11, the combination of Merrell as modified by Hudson teaches the computer system of claim 9, wherein the one or more processors are configured further for operations comprising using a design proposal algorithm for providing a the at least one identified block at the plurality of user terminals (Para. [0066], Markov chain Monte Carlo sampler which is a design proposal algorithm).
With respect to Claim 12, the combination of Merrell as modified by Hudson teaches the computer system of claim 11, wherein the one or more processors are configured further for operations comprising receiving block adjustment inputs at the touchscreen devices of the plurality of user computer terminals to adjust the design proposal (Abstract, Para. [0013], “move any piece of furniture to modify the layout”).
With respect to Claim 13, the combination of Merrell as modified by Hudson teaches the computer system of claim 11, wherein the one or more processors are configured further for operations comprising storing the block adjustment inputs as a design dataset on the computer memory device and refining the design proposal algorithm to one that minimizes block adjustment inputs over the design dataset (Hudson: Para. [0044] – [0046], storing data in databases (220, 230) and using training data to refine the ML engine).
Apparatus claims (14, 15, 16, 17, 18 & 19) are drawn to the apparatus corresponding to the method of using same as claimed in claims (1, 2, 3, 10, 12 & 13). Therefore, apparatus claims (14, 15, 16, 17, 18 & 19) correspond to method claims (1, 2, 3, 10, 12 & 13), and are rejected for the same reasons of anticipation as used above.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Merrell and Hudson as applied to claims 1-6 and 8-19 above, and further in view of Klusza et al. (US 2013/0259308, hereinafter “Klusza”).
With respect to Claim 7, the combination of Merrell as modified by Hudson teaches the computer-implemented method of claim 1.
Merrell fails to expressly disclose:
further comprising generating and displaying, at the computer terminal, a link to an e-commerce platform selling one or more appliances or articles of furniture that conform to the dimensions and any limiting characteristics defining the one or more design elements in each block.
However, Klusza discloses:
further comprising generating and displaying, at the computer terminal, a link to an e-commerce platform selling one or more appliances or articles of furniture that conform to the dimensions and any limiting characteristics defining the one or more design elements in each block (Para. [0016], [0019]).
Therefore, it would be obvious to one of ordinary skill in the art to modify the method, as taught by Merrell, to incorporate object recognition, as taught by Klusza, in order to compare the architectural shape to a pre-defined collection of architectural shapes (Klusza: Para. [0037]).
Response to Arguments/Amendments/Remarks
Applicant’s arguments with respect to claims 1-19 have been considered but are moot because the arguments do not apply to the combination of references used in the current rejection.
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 extension fee 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 date of this final action.
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/BRYAN EARLES/Primary Examiner, Art Unit 2625