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 .
Response to Arguments
Applicant's arguments filed on 4/14/2026 have been fully considered but they are not persuasive for the reasons set forth below:
(1) Applicant has asserted that support for the amendments is in paragraphs [0061] – [0066] and Figure 2, but the specification appears to end at [0052]. Therefore, the cited paragraph support does not spear to exist. As discussed below, the amended independent claims introduce unsupported and indefinite language, so the claims must be further examined as being understood to the extent possible;
(2) Applicant argues that NILAKANTAN (‘697) does not apply because it is directed to machine learning based additive manufacturing. This argument is not persuasive because the pending claims are also directed to multi-dimensional additive printing using object geometry, material requirements, a knowledgebase corpus database, and a machine learning optimization model. As best understood, ‘697 discloses the claimed features as set forth in further detail below; and
(3) Applicant further argues that Tibbets, Jawahir and Apsley do not cure the alleged deficiencies of ‘697. This argument is not persuasive because the secondary references are relied upon for the dependent claim features, while ‘697 is relied upon for the base independent claim limitations. Therefore, in the opinion of the examiner, the Applicant has not presented separate persuasive arguments for the dependent claims.
Claim Objections
Claim 7 is objected to because the phrase “said object comprises a machine learning optimization model optimizing for analyses..” renders claim 1 unclear.
Claim 1 recites an object to be printed and separately recites generating an output model using a machine learning optimization model. Thus, it is unclear whether the claimed object is the printed object, the machine learning optimization model itself, or an object that somehow includes the machine learning optimization model, and it is also unclear what is being optimized or analyzed by the model.
Appropriate correction is required.
Claim Rejections - 35 USC § 112 (new)
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 1-11, 13-18 and 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 claims contain 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.
The originally filed specification does not reasonably convey possession of the amended limitations directed to “detected layers”, dividing such layers into groups based on “relative position in each layers,” and generating the output model based on “analysis and dividing of layers obtained about said object.” While the specification seems to describe dividing layers into groups having length, width, and depth dimensions, it does not seem to adequately describe detected layers, what detects the layers, grouping detected layers based on the claimed relative position, or using such layer division as part of output model generation.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11, 13-18 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claims 1, 13 and 20, “wherein geometry of said includes” is missing a noun after “said”. The claim does not clearly indicate whether the geometry is of the object, components, layers, groups, or something else.
Further, as per claims 1, 13 and 20, “dividing a plurality of detected layers in groups dependent on a relative position in each layers” is confusing because “detected layers” is not adequately defined and the claim does not clearly indicate what detects the layers; further “in each layers” is grammatically awkward; further, “relative position” lacks a clear reference, relative to what exactly?
Further, as per claims 1, 13 and 20, “each group having associated properties emanating from geometry…” is unclear because the claim does not define what the “associated properties” are or what is meant by the properties “emanating” from geometry. The specification seems to support group dimensions, but the claim is worded more broadly and appears indefinite.
Further, as per claims 1, 13 and 20, “output model is based on said information including an analysis and dividing of layers obtained about said object” is confusingly worded. The relationship between the output model, the information, the analysis, the dividing, and the layers is grammatically awkward and unclear.
Claim Rejections - 35 USC § 102 (maintained)
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claims 1-4, 6, 10, 13-15, 17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by NILAKANTAN, U.S. Patent Application Publication No. 2021/0178697 A1 (hereinafter: ‘697).
It is noted that in light of the 112(a) and 112(b) issues, from above, the claims are interpreted as best understood.
As per claim 1, ‘697 discloses a method for multi-dimensional printing, comprising:
receiving a printing request for printing an object having an object geometry, wherein said object includes a plurality of components (e.g., ‘697; [0043], [0044] and [0059], discloses receiving a customer order request to print a part, wherein a part model describes a geometry of the part, including a plurality of subcomponents of the part);
obtaining information relating to specifics of said object to be printed from a knowledgebase corpus database, wherein said information includes at least a material requirement for printing of said object (e.g., ‘697; [0025], [0042] and [0046], discloses obtaining additive manufacturing fabrication parameter data and production outcome data for previous print jobs from an additive manufacturing database, the data including a raw material selected for printing the part);
analyzing said object and determining said object geometry and said at least material requirements for each of said plurality of components for said object, wherein geometry of said includes at least a length, a width, a depth and any angles of connection (e.g., To the extent this limitation can be understood, ‘697; [0046], [0050], [0053] and [0059], discloses analyzing the part to determine geometrical attributes and material selection information for printing the part, wherein the attributes include X-Y-Z dimensions, angles and offsets, and wherein the part includes a main subcomponent and a plurality of protruding subcomponents);
dividing a plurality of detected layers in groups dependent on a relative position in each layers, each group having associated properties emanating from geometry of said object geometry including length, width, and depth (e.g., To the extent this limitation can be understood, ‘697; [0060] and [0062], discloses slicing an STL model of the part into a plurality of layers from bottom to top at a constant slice height, wherein the plurality of layers include top and bottom exposed layers and interior regions of the part that are treated differently based on their relative positions and the geometry of the part);
generating an output model using at least one machine learning optimization model, wherein said output model is based on said information including an analysis and dividing of layers obtained about said object and said analysis of said object's geometry and material requirements (e.g., To the extent this limitation can be understood, ‘697; [0032] and [0056] – [0057], discloses using a machine learning model to generate output information for printing the part, wherein the output information is based on the geometrical attributes of the part, material selection information for printing the part, and layer information for the part, including the plurality of layers of the STL model of the part); and
storing said output model in said knowledgebase corpus database (e.g., ‘697; [0039] and [0042], discloses storing the output information for printing the part in the additive manufacturing database, wherein the additive manufacturing database is updated with part optimization information or command initiation information from the machine learning based additive manufacturing process).
As per claim 2, ‘697 further discloses that the knowledgebase corpus database is used to train at least one Artificial Intelligence (AI) engine (e.g., ‘697; [0056], discloses training the machine learning model using the additive manufacturing database, i.e. the user experience database that stores additive manufacturing data).
As per claim 3, ‘697 further discloses printing said object using said generated output model output (e.g., ‘697; [0067], discloses using a 3D printer to fabricate the part based on the output information from the machine learning model).
As per claim 4, ‘697 further discloses that the printing is three-dimensional (3D) printing (e.g., ‘697; [0056], discloses sending commands to an additive manufacturing device, such as a 3D printer, to print the part).
As per claim 6, ‘697 further discloses that the information about said object to be printed is also received at same time of receiving said printing request (e.g., ‘697; [0043] and [0044], discloses that the customer order request is associated with part information, the part information including required dimensions, geometry curvature use information and the part model).
As per claim 10, ‘697 further discloses that the knowledge base includes input from at least one third party individual (e.g., ‘697; [0042], discloses user’s contributing to the additive manufacturing database by inputting fabrication parameter data and production outcome data).
As per claims 13 and 20, the rational as set forth above with respect to the rejection of claim 1 is applied herein.
As per claim 14, the rational as set forth above with respect to the rejection of claim 2 is applied herein.
As per claim 15, the rational as set forth above with respect to the rejection of claim 4 is applied herein.
As per claim 17, the rational as set forth above with respect to the rejection of claim 10 is applied herein.
Claim Rejections - 35 USC § 103 (maintained)
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over ‘697, as applied to claims 1 and 13, respectively, from above, in further view of Tibbits et al., U.S. Patent Application Publication No. 2015/0158244 A1 (hereinafter: ‘244).
As per claims 5 and 16, ‘697 does not specifically disclose the printing being 4D printing.
‘244 discloses these features by disclosing 4D printing as 3D printing in which the printed part can change shape over time from a first printed shape to a second predetermined shape (e.g., See ‘244; [0006], [0087] and [0089]).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to incorporate ‘244 into ‘697 for the purpose of making printed parts that can change shape over time, and thereby reduce assembly, shipping burden, and fabrication setup costs.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over ‘697, as applied to claim 1, from above, in further view of Jawahir et al., U.S. Patent Application Publication No. 2021/0056473 A1 (hereinafter: ‘473).
As per claim 7, ‘697 does not specifically disclose the object comprising a machine learning optimization model that optimizes analyses of said analyzing a material for each component specifically to determine if said material can be reused.
To the extent this limitation can be understood, ‘473 discloses this feature (e.g., ‘473; [0042], [0049] and [0050], discloses using machine learning based product evaluation and configuration to analyze components and materials to determine how they should be handled for a next use, including reuse or reconfiguration).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to incorporate the teachings of ‘473 into ‘697 so that the material of each subcomponent can be analyzed for reuse, thereby reducing waste and cost and improving manufacturing efficiency.
As per claim 8, ‘697’s combined system (‘697 in view of ‘473) further discloses that the machine learning optimization model also analyses each of said components for sustainability (e.g., ‘473; [0046] - [0047] and [0049], discloses using collected data and machine learning models to assess environmental impact, life cycle cost, and resource consumption, and performance at the module or component level).
As per claim 9, ‘697’s combined system further discloses that the machine learning optimization model also analyses each of said components for environmental impacts associated with usability and waste management (e.g., ‘473; [0042], [0049] and [0051], discloses analyzing environmental impact and customer usage information, and determining how components and materials should be handled for reuse or reconfiguration).
Claims 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over ‘697, as applied to claims 10 and 17, respectively, from above, in further view of Apsley et al., U.S. Patent Application Publication No. 2015/0052024 A1 (hereinafter: ‘024).
As per claims 11 and 18, although ‘697 adequately discloses that the one or more third-party individuals include at least designers and engineers (e.g., See ‘697; [0042] and [0049]; which discloses users contributing to the additive manufacturing database and a part designer providing the CAD model for the part), ‘697 does not adequately disclose that the third-party individual also include material source providers of the subcomponents of the part to be printed.
‘024 discloses this missing feature by disclosing third party suppliers/manufacturers providing physical item and/or 3D manufacturing instructions, including base files and part files with specifications from a manufacturer, for use in printing (e.g., See ‘024; [0039] and [0067]).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to incorporate the teachings of ‘024 into ‘697 for the purpose of obtaining reliable supplier provided component files and specifications, thereby reducing errors and rework, and ensuring correct component information is used instead of relying only on user entered data.
References Considered but Not Relied Upon
The following references were considered but were not relied upon with respect to any prior art rejections:
(1) US 2015/0331402 A1, which discloses building a print profile by matching part features and material data against a database, then updating it using the results;
(2) US 2018/0341248 A1, which discloses using machine learning and sensor data to spot defects and adjust additive manufacturing settings in real time;
(3) US 2019/0054700 A1, which discloses learning from measured part dimensions to build a regression model and adjust G-code and print parameters;
(4) US 10,684,806 B2, which discloses matching a print job’s parameters to a suitable printer and slices the model to create updated printing instructions; and
(5) US 9,855,698 B2, which discloses using camera and machine learning to predict good slicer settings, detect failures early, and improve future prints.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONALD D HARTMAN JR whose telephone number is (571)272-3684. The examiner can normally be reached M-F 8:30 - 4:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at (571) 272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RONALD D HARTMAN JR/Primary Patent Examiner, Art Unit 2119 June 18, 2026
/RDH/
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