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 .
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.
Response to Amendment
Applicant’s amendment filed on 19 May 2026 has been entered. Claims 1, 11-14, and 17 are amended. Claims 1-20 are pending.
Applicant’s arguments, filed 19 May 2026, with respect to the rejection(s) of Claims 1-13 and 16-20 under 35 U.S.C. § 102(a)(1) as being anticipated by EP Patent No. 2793151 (Goh) and claims 14 and 15 under 35 U.S.C. § 103 as being unpatentable over Goh in view of U.S. Patent Publication No. 2022/0390918 (Atev) have been fully considered and are persuasive.
Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Fox et al. (US 20240185531).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Goh (EP 2793151A1) in view of Fox et al. (hereafter Fox – US 20240185531).
Claim 1 recites “a method of manufacturing an electronic device peripheral.” Goh teaches such a method, as will be shown.
Goh teaches (Figs. 1-7) a method of manufacturing an electronic device peripheral, the method comprising:
obtaining a model of a peripheral, the model having a plurality of components (para. 0030, 0037-0038, 0045-0046);
identifying one or more fit components of the model (para. 0016-0017, 0027-0028);
identifying one or more functional components (para. 0024, 0027-0028, 0037-0038);
receiving at least one change to the model (para. 0033-0036);
based at least partially on the at least one change, changing at least one property of a fit component of the one or more fit components without altering a functional component of the plurality of components (para. 0030, 0039, 0051-0052, 0096-0099); and
providing at least an altered fit component to an additive manufacturing device (para. 0028, 0047, 100-103).
However, Goh does not teach obtaining a computer assisted design (CAD) model of the peripheral; identifying one or more fit components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system; identifying one or more functional components of the CAD model; receiving at least one change to the CAD model.
Fox teaches a method of manufacturing an electronic device peripheral (Fig. 2) comprising obtaining a computer assisted design (CAD) model of the peripheral (205, para. 0040); identifying one or more fit components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system (fit components do not diminish functionality when altered, see para. 0035, 0042-0045); identifying one or more functional components of the CAD model (functional components are identified so they are not altered, para. 0042, 0045); receiving at least one change to the CAD model (para. 0035, 0042-0045).
Fox further teaches such a method results in faster rapid prototyping where modification may be done in such a way that it primarily or exclusively relates to enabling the desired interfacing with the desired object, such that the modification does not diminish functionality or alter dimensions that do not relate to that desired interfacing. That is, the 3D model may be modified with respect to some aspects, but constrained with respect to other aspects (para. 0034-0035).
It would have been obvious for a person having ordinary skill in the art to apply the teachings of Fox to the method of Goh to have obtaining a computer assisted design (CAD) model of the peripheral; identifying one or more fit components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system; identifying one or more functional components of the CAD model; receiving at least one change to the CAD model, as both references and applicant’s invention are directed to methods of manufacturing an electronic device peripheral. Doing so would result in faster rapid prototyping where desirable modifications may be made without diminishing functionality, as recognized by Fox.
Regarding Claim 2, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 1, wherein the at least one property of the fit component includes a dimension of the fit component (para. 0029-0041).
Regarding Claim 3, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 1, wherein the at least one property of the fit component includes a material property of the fit component (para. 0055-0056).
Regarding Claim 4, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 3, wherein changing the material property changes an elastic modulus property of the fit component (para. 0055-0056).
Regarding Claim 5, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 1, wherein the at least one property of the fit component includes a perimeter shape property of the fit component (para. 0031-0032, 0043-0044).
Regarding Claim 6, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 1, wherein the at least one property of the fit component includes a mass property of the fit component (para. 0055, materials may have different masses, recesses would change the mass property).
Regarding Claim 7, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 1, wherein the one or more functional components includes one or more of a mechanical switch, a magnetic switch, a light switch, and a potentiometer (para. 0041-0046).
Regarding Claim 8, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 1, further comprising additively manufacturing a part of the peripheral corresponding to the altered fit component (para. 0028, 0047-0049).
Regarding Claim 9, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim I, wherein providing at least an altered fit component includes sending the altered fit component via a network from a client device (para. 0054, 0063-0065).
Regarding Claim 10, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 1, wherein the one or more functional components includes a connection interface (para. 0027, 0052, 0055).
Regarding Claim 11, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 1, wherein the change to the CAD model includes a change to a dimension of the model (para. 0029, 0033-0035, 0063).
Claim 12 recites “a method of manufacturing an electronic device peripheral.” Goh teaches such a method, as will be shown.
Goh teaches (Figs. 1-7) a method of manufacturing an electronic device peripheral, the method comprising:
obtaining a model of a peripheral (para. 0030, 0037-0038, 0045-0046);
identifying one or more functional components of the model (para. 0024, 0027-0028, 0037-0038);
identifying at least one remaining component of the model that is not a functional component of the one or more functional components of the model (para. 0016-0017, 0027-0028);
designating one or more of the at least one remaining component of the model as a fit component (para. 0016-0017, 0027-0028);
receiving at least one change to the model (para. 0033-0036);
based at least partially on the at least one change, changing at least one property of the fit component without altering a functional component (para. 0030, 0039, 0051-0052, 0096-0099); and
providing at least an altered fit component to an additive manufacturing device (para. 0028, 0047, 100-103).
However, Goh does not teach obtaining a computer assisted design (CAD) model of the peripheral; identifying one or more fit components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system; identifying one or more functional components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system; receiving at least one change to the CAD model.
Fox teaches a method of manufacturing an electronic device peripheral (Fig. 2) comprising obtaining a computer assisted design (CAD) model of the peripheral (205, para. 0040); identifying one or more fit components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system (fit components do not diminish functionality when altered, see para. 0035, 0042-0045); identifying one or more functional components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system (functional components are identified so they are not altered, para. 0042, 0045); receiving at least one change to the CAD model (para. 0035, 0042-0045).
Fox further teaches such a method results in faster rapid prototyping where modification may be done in such a way that it primarily or exclusively relates to enabling the desired interfacing with the desired object, such that the modification does not diminish functionality or alter dimensions that do not relate to that desired interfacing. That is, the 3D model may be modified with respect to some aspects but constrained with respect to other aspects (para. 0034-0035).
It would have been obvious for a person having ordinary skill in the art to apply the teachings of Fox to the method of Goh to have obtaining a computer assisted design (CAD) model of the peripheral; identifying one or more fit components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system; identifying one or more functional components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system; receiving at least one change to the CAD model, as both references and applicant’s invention are directed to methods of manufacturing an electronic device peripheral. Doing so would result in faster rapid prototyping where desirable modifications may be made without diminishing functionality, as recognized by Fox.
Regarding Claim 13, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 12, wherein the CAD model is a unitary model when obtained (para. 0048, model may be separate or unitary).
Regarding Claim 14, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 12, wherein identifying at least one remaining component includes inputting at least a portion of the CAD model into the ML system (see Fox Fig. 2).
Regarding Claim 15, modified Goh teaches (Goh Figs. 1-7) the method of claim 14, wherein the ML system identifies at least one connection interface (Goh para. 0027, 0052, 0055).
Regarding Claim 16, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the method of claim 12, wherein the peripheral is an adaptive electronic device peripheral (para. 0079, 0089).
Claim 17 recites “a system for manufacturing an electronic device adaptive peripheral.” Goh teaches such a system, as will be shown.
Goh teaches (Figs. 1-7) a system for manufacturing an electronic device adaptive peripheral, the system comprising:
an additive manufacturing device (para. 0100, 3D printer);
a computing device in data communication with the additive manufacturing device, the computing device including (para. 0100, computer):
a processor (para. 0100);
a communication device in data communication with the processor (para. 0100);
a hardware storage device in data communication with the processor, the hardware storage device having instructions stored thereon that, when executed by the processor, cause the computing device to (para. 0100):
obtain a model of a peripheral, the model having a plurality of components (para. 0030, 0037-0038, 0045-0046);
identify one or more fit components of the model (para. 0016-0017, 0027-0028);
identify one or more functional components (para. 0024, 0027-0028, 0037-0038);
receive at least one change to the model via a network (para. 0033-0036);
based at least partially on the at least one change, change at least one property of a fit component of the one or more fit components without altering a functional component of the plurality of components (para. 0030, 0039, 0051-0052, 0096-0099); and
send a transmission to the additive manufacturing device to manufacture a part of the peripheral corresponding to an altered fit component (para. 0028, 0047, 100-103).
However, Goh does not teach obtaining a computer assisted design (CAD) model of the peripheral; identifying one or more fit components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system; identifying one or more functional components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system; receiving at least one change to the CAD model.
Fox teaches a method of manufacturing an electronic device peripheral (Fig. 2) comprising obtaining a computer assisted design (CAD) model of the peripheral (205, para. 0040); identifying one or more fit components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system (fit components do not diminish functionality when altered, see para. 0035, 0042-0045); identifying one or more functional components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system (functional components are identified so they are not altered, para. 0042, 0045); receiving at least one change to the CAD model (para. 0035, 0042-0045).
Fox further teaches such a method results in faster rapid prototyping where modification may be done in such a way that it primarily or exclusively relates to enabling the desired interfacing with the desired object, such that the modification does not diminish functionality or alter dimensions that do not relate to that desired interfacing. That is, the 3D model may be modified with respect to some aspects but constrained with respect to other aspects (para. 0034-0035).
It would have been obvious for a person having ordinary skill in the art to apply the teachings of Fox to the method of Goh to have obtaining a computer assisted design (CAD) model of the peripheral; identifying one or more fit components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system; identifying one or more functional components of the CAD model by inputting at least a portion of the CAD model into a machine learning (ML) system; receiving at least one change to the CAD model, as both references and applicant’s invention are directed to methods of manufacturing an electronic device peripheral. Doing so would result in faster rapid prototyping where desirable modifications may be made without diminishing functionality, as recognized by Fox.
Regarding Claim 18, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the system of claim 17, wherein the at least one property of the fit component includes a material property, and the transmission to the additive manufacturing device includes a request to change filament material (para. 0055).
Regarding Claim 19, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the system of claim 17, wherein the computing device is local to the additive manufacturing device (para. 0063).
Regarding Claim 20, Goh, as modified with Fox above, teaches (Goh Figs. 1-7) the system of claim 17, wherein the transmission to the additive manufacturing device further causes the additive manufacturing device to manufacture a second part of the peripheral corresponding to a functional component (para. 0049).
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See cited references.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW BUI whose telephone number is (571) 272-0685. The examiner can normally be reached on 7:30 AM - 4:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Courtney Heinle can be reached on (571) 270-3508. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ANDREW THANH BUI/Examiner, Art Unit 3745
/COURTNEY D HEINLE/Supervisory Patent Examiner, Art Unit 3745
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