DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims1-20 have been presented for examination based on the application filed on 6/30/2026.
Claims 1, 8, 11 and 18 are amended.
Rejection for Claims 8 & 18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph is WITHDRAWN in view of amendment.
Claim(s) 1-8, and 11-18 remain rejected under 35 U.S.C. 103 as being unpatentable over US 20240192658 A1 by NGUYEN; HOAI THANH, in view of US 20200096978 A1 by BAKER; Robert Anthony.
Claim(s) 9-10 remain rejected under 35 U.S.C. 103 as being unpatentable over US 20240192658 A1 by NGUYEN; HOAI THANH, in view of US 20200096978 A1 by BAKER; Robert Anthony, further in view of NPL by D. Mittel et al, "Ontology-Based CAD Analysis with LLMs: Natural Language Querying of Boundary Representations," 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), Porto, Portugal, 2025, pp. 1-8.
This action is made Final.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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Response to Arguments
While amendment provided by applicant required reconsideration of the rejection under 35 USC 103, the prior art Nguyen still appears to teach the limitations of the claim as newly mapped below. Nguyen teaches mortice and tenon joints in assembly (Figs.20A-20G & [0245]-[0246]) and uses the rules ([0207]-[0212]) to perform evaluation for assembly and manufacturability, and in the end providing the manufacturing of the furniture. No new arguments are made other than restating the independent claims are not taught. Additionally no new arguments are made for dependent claims. Examiner respectfully maintains the rejection.
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Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-8, and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20240192658 A1 by NGUYEN; HOAI THANH, in view of US 20200096978 A1 by BAKER; Robert Anthony.Regarding Claims 1 & 11 (Updated 7/23/26)
Nguyen teaches
(Claim 1) A method for automatic design and manufacture of furniture (Nguyen: Fig.1 & Fig.9 [0150]-[0164] Abstract) ,
(Claim 11) A computer for automating design and manufacture of furniture, comprising: a memory for storing instructions; and one or more processors that execute the instructions to cause actions (Nguyen: Fig.1 and Fig.22) , including/comprising:
collecting design information for one or more items of furniture based on content and information received from one or more sources (Nguyen: Fig. 9 step 902 [0152] "... input design specification: (1) by written description; (2) voice activation; (3) incomplete or partial design work; and (4) photo images. These different input specification formats are translated and/or then digitized into vector files containing of bits and pixels. Furthermore, these inputs are either stored in databases, imported from the Internet, and/or designer's direct input to graphic area 301, from chat forum 243 and/or auxiliary display panel 360...."; [0106] "... Training and evaluation datasets with 100, 200, 400, 800, and 6,000 images are constructed separately through data processing....") ;
employing one or more user interfaces to display one or more preview visualizations of the one or more items of furniture based on the design information (Nguyen: Fig.9 Step 904 "... if the input design specification is decoded and the design work exists in a database then algorithm 900 goes to step 915 which displays the retrieved design work in graphic area 901 for review and/or edit. As a non-limiting example, if the engineering specification describes a chair such as chair 350. If the description or design work of chair 950 is decoded and understood, chair 950 is retrieved and displayed on graphic are 901...."; the flow steps 905—911 is equally applicable when the chair is not in database) , wherein the display of the one or more preview visualizations is iteratively updated1 based on one or more edits to one or more of the design information, or the one or more preview visualizations (Nguyen: [0090] "... Often, at the beginning of the design process, a user does not have clear idea about a design. In this situation, this user can use write 345 to jot down his/her initial ideas. Then use suggest 342 to view an initial design on design area 301. Rotate 327, edit 322, and select 325 can be used to achieve a better idea of the design. Audio 326 is another method to record the design idea and suggest 342 to provide an design work specification. As a non-limiting example, when a user uses audio 346 to provide a chair with a back support and four legs. A voice recognition algorithm of auto-mode 341 translates this voice command into codes. Based on this information, suggest 342 uses machine learning algorithm to provide design work 350...." [0091] "... [0091] In another situation, import 347 allows users to import images of workpieces such as design work 350 from the world-wide-web, local databases, network-databases, and social media. Computer vision algorithms of smart-mode 341 recognize these imported images and convert them into codes that can display on design area 301. ..."; [0092] "... The world wide-web 333 allows the designer to search the web for their favorite designs....") ;
employing an approval of a design evaluation report to automatically use a plurality of rules to model one or more physical constraints (Nguyen : [0140][0207]-[0213] rules for joints Also see Fig.19B and 20A-20G discussing rules of assembly in [0246]-[0252]; Fig.1 & Fig.15 [0228]-[0234]; Fig.2 see smart manufacture 251 with RCNN-based smart mode; Nguyen : [0060] "... FIG. 1 presents an overview of six main functions of software application program 100. These main functions, among other functions, include (1) start a manual and/or smart mode on a graphic area; (2) recommend or complete an design work; (3) determine the relationship among components of an design work including automatic counteracting and smart fitting;..."); [0067] "... In some features of method 100 of the present invention, the smart mode of step 104 can classify a component including a detecting a wooden part and connection parts and then presenting the automatic counteraction. Automatic counteraction includes geometry, dimension, number of wooden parts, types of joints (e.g., basic butt), number of joints, locations of joints, and angle of insertion—these are features that are detected by different filters of CNN algorithms except dimensions...."; In context of Fig.6 running RCNN steps 620-623 to complete the joinery; Also see Fig.4-6 how RCNN does parts joinery) for a plurality of particular mortice and tenon at one or more locations (Nguyen : Fig.19B showing one such joint, 20A showing plurality of joints 2005-1 to 2005-4 joint to 2001 & 2002:
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) in one or more sub-components with one or more material (Nguyen : Fig.20A-20G showing sub-components individually labelled) materials for the one or [more] items of furniture (Nguyen: Fig.19A, Table 1 see element 16, 33, 55 showing various mortise and tenon configurations as one of the joint types RCNN uses in automatically completing the joinery; [0102] "... The machine cutting dataset collected by hand and labeled, is used to train RCNN 400 described above. Again, RCNN 400 above outputs a sequence of components with connectors and their automatic counteractions, i.e., complementary connectors. For example, if the j component is a male, RCNN 400 after trained by the machine cutting dataset in Table 1 would output a j+1 component with female connector....");
employing a simulation (Nguyen: [0060] "...These main functions, among other functions, include (1) start a manual and/or smart mode on a graphic area; (2) recommend or complete an design work; (3) determine the relationship among components of an design work including automatic counteracting and smart fitting; (4) generate a step-by-step assembly instructions and a bill of materials (BOM); (5) model, simulate, and test the complete design work;...") of the one or more physical constraints for the plurality of selected mortice and tenon compression joins (Nguyen : Figs. 19B & [0245] and Figs. 20A-20G) to validate their feasibility in manual assembly (Nguyen : [0207]-[0212]) of the one or more sub-components for the one or more items of furniture by a user (Nguyen : [0140] "... [0140] At step 814, based on the memory and attention mechanism of the recurrent neural network, smart fitting is performed. In smart fitting, if the designer [user] changes the dimension of the assembly, other connected components are automatically changed unless they must stay the same according to rules and regulations...") without use of one or more of a fastener or an adhesive (Nguyen: See Fig.19A of joint without mention of adhesive or fastener [0244]) ;
obtaining a three-dimensional visualization animation for assembly of the one or more sub- components (Nguyen : [0159]"... Assembling drawings of the present invention include 3D modeling, views or orientation, components, connectors, geometrical shape and sizes of each component. They all have Cartesian coordinates. Step 908 uses these information to generate a blocking list or an occlusion lists. The blocking lists show which connectors or which parts are blocked by other components if they are not connected first. Each group of components has a blocking list. Then, based on the blocking list, group ID, component ID, connector ID, geometrical shapes and sizes, and their coordinates and the above ID codes, step 908 generates either a video animation or a step-by-step assembly instructions....") with the plurality of validated mortice and tenon compression joins in a sequence of steps that demonstrate how to manually assemble the one or more items of furniture(Nguyen : Fig.20A-20G showing plurality of mortice and tenon joints assembled for the chairs as discussed in Fig.9 flow steps 905-911 & [0155]-[0161]);
employing the design evaluation report to automatically initiate manufacture of the one or more sub-components with the plurality of validated mortice and tenon compression joins for the one or more items of furniture (Nguyen:[0078] showing review and visualization"... Manual mode module 210 includes a design unit 214, a design and review evaluation 213, …” ; Fig.9 flow step 912 & [0163]) ;
and
employing the one or more user interfaces to present one or more display panels for content that includes one or more of the design evaluation report (Nguyen: [0078] showing review and visualization"... Manual mode module 210 includes a design unit 214, a design and review evaluation 213, an engineering analysis unit 212, and a geometric modeling unit 211. Design unit 214 generates design work interface (EDI) which includes interactive graphic 201. Design and review evaluation unit 213 allows a designer to finalize and change a design work, which is part of EDI...."); Fig. 20A-20G showing a 3D sequence diagram demonstrating assembly instructions, bill of materials (BOM), and smart manufacturing 2000 in accordance with an embodiment of the present invention is illustrated; Fig.3 [0089] "... [0089] Continuing with FIG. 3, in one exemplary embodiment of the present invention, a bottom horizontal tool bar 340 of EDI 300 is comprised of the artificial intelligent functions such as smart-mode 341, suggest 342, forum 343, video 344, write 345, bill of materials (BOM) 346, an import 347, display 348, and simulation 349...."); further design evaluation reports as graphs of simulation [0091] "... Evidently, the real-time manufacturing of selection 325-1 can be seen on display 360. Finally, simulation 349 provides engineering analyses including finite of design work 350 including forces, torques, materials, joints, balance, etc. The graphs and numerical results of this simulation can be displayed on display 360...."), the three-dimensional visualization animation (Nguyen: Fig.20A-20G & [0246]; [0159]"... Assembling drawings of the present invention include 3D modeling, views or orientation, components, connectors, geometrical shape and sizes of each component. They all have Cartesian coordinates. Step 908 uses these information to generate a blocking list or an occlusion lists. The blocking lists show which connectors or which parts are blocked by other components if they are not connected first. Each group of components has a blocking list. Then, based on the blocking list, group ID, component ID, connector ID, geometrical shapes and sizes, and their coordinates and the above ID codes, step 908 generates either a video animation or a step-by-step assembly instructions...."; [0242], [0244]), a recommendation, or other information associated with the one or more items of furniture, wherein the content is dynamically transformed and arranged for display to the user based on one or more of user interaction telemetry, user feedback or telemetry metrics (Nguyen: Fig.3 [0083]-[0093]; Fig. 20A-20G) .
Nguyen does not teach join[t] to be compression joint specifically. Although it should be noted it contains reference to over 6000 joint types used by RCNN some of which are listed in Table-1.
Baker teaches compression joint specifically used in designing the CAD as hammer head mortise and tenon joint 602 or splice joints 606 (Baker: See Fig. 6 & 7 [0098]-[0100], and without nails or screws - [0113]:
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).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Baker to Nguyen to further include compression joints in the list of 6000+ joints to train the automatic joint configuration complementing Nguyen’s RCNN (Baker: [0100]; Nguyen: [0082][0111]) . Further motivation to combine would have been that Nguyen and Baker are analogous art to the instant claim in the field of computer based automated CAD & CAM (Baker: Fig.1 Nguyen: Fig.1).
Regarding Claims 2 & 12
Baker (specifically for the compression based mortise and tenon) & Nguyen (in general using RCNN – [0111])) both teaches method of Claim 1/11, wherein forming the plurality of mortice and tenon compression joins, further comprises: manufacturing each compression join based on one or more of a size, a weight, a shape, a material, or a clearance of each sub-component of the item of furniture, wherein the plurality compression joins are used to assemble each sub-component into the item of furniture (Baker: [0100] "... The number of joints and the spacing between each joint (shown in FIG. 21) may be formulaically calculated from parameters defined in the design phase. Software may compute machine code for all joints and parts required to construct each unit from flat sheets and/or boards of non-metal material (such as plywood and solid hardwood). Parts may be algorithmically arranged in a two-dimensional plane with x and y lengths equivalent to sizes of physical material to be machine cut (This function may be called nesting). Software may then output machine readable digital files for a computer numerical control (CNC) routing machine, as shown in FIG. 8...."); parameters as dimensions and alignment – [0085]-[0086], [0094], [0105]-[0106]; non-dimensional parameters – [0142]).
Regarding Claims 3 & 13
Baker teaches the method of Claim 1/11, further comprising: automatically transmitting an invoice to the user for the one or more items of furniture based on the approval of the design evaluation report, wherein the invoice includes one or more costs for manufacture (Baker: [0109]"... Alternatively, the software may send bills of materials to centers...."; Nguyen: [0027] [0065] teaches creation of bill of material) and delivery of the one or more items of furniture to the user (Baker: [0089]-[0090] "... This may allow the company to create programs that automatically design and deliver a model for a custom unit to the manufacturer irrespective of the manufacturer's level of experience assembling like units....")
Regarding Claims 4 & 14
Nguyen teaches the method of Claim 1/11, wherein the automatic initiation of manufacture, further comprises: obtaining a plurality of computer numerical control (CNC) instructions to direct one or more manufacturing machines (Nguyen : Fig.1 element 107; Fig.8 Steps 817-818, Abstract & [0067] showing CNC instructions being generated for joints; mortise and tenon – [0085] Table 1; Fig.19A-B) to form the plurality of particular mortice and tenon (Nguyen: [0091]) . Baker teaches manufacturing of compression joints (Baker: Fig.6 & 7 as mapped above)
Regarding Claims 5 & 15
Nguyen & Baker teaches the method of claim 1/11, wherein the one or more materials further comprise: a solid material, including veneer plywood, plywood, solid wood (Nguyen: [0085] wood, [0132]) , particle board wood (Baker: [0100] plywood) , metal (Nguyen: [0085] aluminum, [0132]) , plastic (Nguyen: [0132]) , stone, polymer, or composite materials; or a state change material associated with a state change from a liquid to a solid at ambient temperatures, including concrete, metal, ceramics, plastic, polymers, or composite material.
Regarding Claims 6 & 16
Baker teaches the method of Claim 1/11, further comprising: employing one or more computer numerical control (CNC) machines to automatically manufacture the one or more items of furniture from one or more of a solid material, a state change material, or a printable material (Baker: Abstract & Title [0088]-[0093]) .
Regarding Claims 7 & 17
Nguyen and Baker teaches the method of Claim 1/11, further comprising: obtaining one or more recommendations to update the design of one or more items of furniture based on one or more of heuristic information, cost, or a type of material (Nguyen: [0013] [0066] "... More particularly, step 103 is fructified by a smart mode realized by recurrent convolutional neural network (RCNN) algorithms that automatically recommend and/or predict any components, sub-assembly, or even the entire design work. More particularly, the RCNN uses its feature detection capability to classify a component (i.e., hind legs of a chair) including its wooden part and a joinery part. The RCNN uses its sequential processing of data to recommend the assembly order and the bill of materials (BOM) of the design work...." and Baker: [0130] "... In much the same way that a design professional may provide recommended design solutions as well as alternatives to a client, the generative design algorithm may present the customer (user of the software) with suggested designs for consideration....") .
Regarding Claims 8 & 18 (Updated 7/23/26)
Baker teaches the method of Claim 1/11, further comprising: obtaining one or more adhesives to be used in addition to one or more portions of the plurality of compression joins, wherein the one or more adhesives are additionally employed during assembly to increase difficulty of subsequent disassembly of the one or more sub-components of the one or more items of furniture (Baker: [0104]).
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Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240192658 A1 by NGUYEN; HOAI THANH, in view of US 20200096978 A1 by BAKER; Robert Anthony, further in view of NPL by D. Mittel et al, "Ontology-Based CAD Analysis with LLMs: Natural Language Querying of Boundary Representations," 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), Porto, Portugal, 2025, pp. 1-8.
Regarding Claims 9 & 19
Nguyen teaches the method of Claim 1/11, further comprising: training one or more (Nguyen: [0062]-[0063] Fig.6 steps 601-602, 603, 610-612 training the RCNN) ; and employing the one or more trained large language models to obtain an intermediate design of the one or more items of furniture (Nguyen: Fig.6 Steps 620-623; Furniture).
Nguyen teaches using various forms of inputs (Nguyen: [0153] "... In many aspects of the present invention, step 903 affords the designers to provide different content information including text, image, audio, and even video inputs. If the design specification is in written text, text recognition engine is used to understand the input text....").
Nguyen and Baker do not teach LLM specifically.
Mittel teaches training one or more large language models with the collected design information (Mittel : Abstract §II.C and §IV & Fig.3 ) ; and employing the one or more trained large language models to obtain an intermediate design of the one or more items (Mittel : See Fig.3 flow leading to model; geometric object can be furniture is immaterial to the LLM based query addressing the geometric feature).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Mittel to Nguyen to implement text/voice based searching of CAD models overcoming the problems disclosed in Mittel (Mittel: Introduction requiring large training databases interfacing with CAD to create knowledge graphs) . Further motivation to combine would have been that Mittel and Nguyen are analogous art to the instant claim in the field of CAD model search based on natural language query (Mittel: See Fig.3, Nguyen: [0153]).
Regarding Claims 10 & 20
Mittel inherently teaches the method of Claim 9, further comprising employing a model context protocol (MCP) to collect the design information to train the one or more large language models (Mittel: §V.A teaches LLM are implemented using Anthropic’s Clause Opus 4 model. Claude Opus uses MCP2, Succinctly MCP is to AI what API are to client-server based applications) .
Motivation to combine is as presented in claim 9.
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Relevant Prior Art
Exemplary Claims 9-10 can also be rejected with US 20240004424 A1 by Wickersham; James teaching "... he structural configuration system is configured to, and can, use (and/or include) a large language model (LLM). A LLM used by the structural configuration system refers to language model including at least one ML model that is trained, using training content, to generate content associated with a housing element, interlocking component, and/or tooling... Examples of LLMs include Generative Pre-Trained Transformer (GPT) (e.g., GPT-2, GPT-3, GPT-3.5, GPT-4, or further iteration(s) of GPT) DaVinci, an LLM using LangChain (MIT), or a combination thereof. "
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKASH SAXENA whose telephone number is (571)272-8351. The examiner can normally be reached Mon-Fri, 7AM-3:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RYAN PITARO can be reached on (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Thursday, July 23, 2026
1 US 20190121321 A1 by Dew; Daniel et al. also teaches Iterative updating in Fig.2C & ¶[0081]-[0085], to select a design for furniture plan generation based on iteratively updating user interface based on user edits. This prior art may be used in future rejections.
2 Article “Unlocking the power of Model Context Protocol (MCP) on AWS” dated June 3rd 2025 by AWS cited on PTO892,
Prosecution Insights