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 .
Claims 1-15 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/14/24 was considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
Claim Rejections - 35 USC § 102
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.
4. 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
5. Claims 1 and 3-15 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Shetty (Shetty), US publication no. 2022/0011743 A11.
As per claim 1, Shetty discloses an apparatus for printing three-dimensional, 3D,
objects by a 3D printer [figure 1; para 23], the apparatus comprising:
interface circuitry configured to receive a G-code file comprising instructions for 3D printing of one or more 3D objects [figure 1; para 7, 8, 23, 30]; and
processing circuitry configured to classify a 3D object represented by the G-code file, and restrict 3D printing of the 3D object if the 3D object is classified as at least a part of a dangerous/illegal object [figure 5; para 28, 30, 58, 74].
Shetty teaches:
[0028] The lists can be set up by an administrator at the management server, in an example. However, machine learning can also update the lists based on manual overrides that occur. In one example, if the model 126 categorizes an object 114 in such a way that it is denied from printing based on a list, the print server 120 can send a notification to the management server. An administrator can review the denied
request and override the denial. Override decisions can be stored by the system. An algorithm can analyze the overrides and recognize trends in which object types are commonly allowed even though not present in a whitelist or present in a blacklist. The algorithm can then adjust the appropriate lists, in an example. For example, if a gear is commonly rejected but given override acceptance, a gear classification can be added to a whitelist or removed from a blacklist. These updated lists can then be provided to the print server 120.
[0030] In one example, the model 124 can run recognition on the G-code ( or corresponding code instructions) of the object 114. It can do this by comparing sections of the G-code to known templates of malicious objects. As an example, the model 124 can check the G-code of the object 114 for certain G-code instructions that are unique to any blacklisted or whitelisted objects. In one example, the unique G-code instructions can be used to analyze the object 114 and increase prediction accuracy in case a print job partially resembles a malicious object.
[0058] In one example, at stage 310 the model 132 can recognize the object 114 as a possible component of a blacklisted item. Then, at stage 312, the model 132 can apply
a flag to the object 114 identifying the blacklisted item and a component of the blacklisted item that the object 114 corresponds to. Then at stage 312, the model 132 can analyze the object 114 in combination with any previously printed objects flagged for the same blacklisted item. At stage 314, the server can compare the combination to a blacklist. If the combination of the object 114 and any previously printed objects amounts to more than a certain threshold amount of a blacklisted item, the server can deny the printing request.
As per claim 3, Shetty discloses the G-code file specifies coordinates, wherein the processing circuitry is configured to classify the 3D object based on spatial dimensions of a geometric feature of the 3D object corresponding to the coordinates [para 30].
As per claim 4, Shetty discloses the processing circuitry comprises a trained machine learning network having an input for the G-code file and an output for a
classification signal that is indicative of whether or not the 3D object represented by the G-code file is dangerous/illegal [para 24, 25].
As per claim 5, Shetty discloses the trained machine learning network is configured to map the G-code file to an object type of dangerous/illegal 3D objects if it is determined that the G-code file corresponds to a dangerous/illegal 3D object [para 38, 42, 43].
As per claim 6, Shetty discloses ratus of claim 5, wherein the object type
corresponds to at least one of a barrel, frame, receiver, slide, magazine, hammer, handle, cartridge, trigger, spring, connecting rod, and pin of a gun [para 33].
As per claim 7, Shetty discloses the trained machine learning network is configured to further update its model parameters based on incremental training data including one or more new G-code files corresponding to dangerous/illegal objects not included in previous training data [para 29, 38].
As per claim 8, Shetty discloses the trained machine learning network is based on an attention-based convolutional neural network [para 31, 42].
As per claim 9, Shetty discloses the interface circuitry is configured to receive a request for 3D printing of the 3D object according to the G-code file, and wherein
restricting 3D printing of the 3D object by the processing circuitry includes at least one of: requiring 3D printing to be done through a user profile [para 11, 23]; sending and/or storing a notification of the request and/or instance of3D printing of the 3D object associated with the user profile; performing 3D printing of a modified version of the 3D object, and blocking the request for 3D printing of the 3D object [para 32, 33, 58].
As to claims 10-15, claims 1 and 3-7 basically are the corresponding elements that are carried out the method of operating step in claims 10-15. Accordingly, claims 10-15 are rejected for the same reason as set forth in claims 1 and 3-7.
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.
6. 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.
7. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Shetty (Shetty), US publication no. 2022/0011743 A1 in view of Isbjornssund et al (Isbjornssund), US publication no. 2014/0058959 A12.
As per claim 2, Shetty fails to disclose the G-code file specifies a path of a tool of the 3D printer, and wherein the processing circuitry is configured to classify the 3D object based on spatial dimensions corresponding to the path.
Isbjornssund discloses the G-code file specifies a path of a tool of the 3D printer, and wherein the processing circuitry is configured to classify the 3D object based on spatial dimensions corresponding to the path [para 108, 130].
It would have been obvious to one of ordinary skill in the art at time the invention to combine the teachings of Shetty and Isbjornssund because they both disclose a 3D printing system, the specify teachings of Isbjornssund stated above would have further enhanced the performance and function of Shetty system to obtain predictable results to specify a path of a tool of 3D printer.
8. Examiner's note: Examiner has cited particular paragraphs and columns and line numbers in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS."
9. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Vassil et al,, US publication no. 2020/0269511, teaches a method for prevention of black-listed parts from being printed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHUN CAO whose telephone number is (571)272-3664. The examiner can normally be reached on M-F 7:30 am-4:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached on 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHUN CAO/Primary Examiner, Art Unit 2115
1 Shetty is cited by applicant.
2 Isbjornssund is cited by applicant.
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