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 .
Claim Objections
Applicant needs to address the following issues and appropriate changes are required:
Claim 3, “said generated output model output” lacks proper antecedent basis; claim 1 introduces “an output model” not an “output model output”;
Claim 7, “said material” lacks proper antecedent basis; claim 1 recites “material requirement(s)”, not “material”. Further, “said object comprises a machine learning optimization model optimizing for analyses …” is unclear, its unclear whether the printed “object” is the ML model itself and what exactly is being optimized/analyzed;
Claim 11, “said third party individuals” lacks proper antecedent basis; claim 10 introduces “at least one third party individual”; and
Claim 20, the preamble stating “cleansing steps for using a plurality of different transformation assets,” conflicts with the body of the claim and is inconsistent with the flow and its scope or intent is unclear and confusing.
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 (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, 12-15, 17, and 19-20 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).
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., See ‘697; [0043], [0044], [0048] and [0059], which collectively disclose a customer order beginning the printing process, wherein a model defines a shape of the object having multiple subcomponents);
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., See ‘697; [0025], [0042] and [0046], which collectively disclose the system checking its knowledge base database to get printing settings, including which material to use);
analyzing said object and determining said object geometry and said at least material requirements for each of said plurality of components for said object (e.g., ‘697; [0059] and [0062], which collectively disclose analyzing the STL model (including subcomponents) by slicing cross sections, with slice height determined based on the printing material);
generating an output model using at least one machine learning optimization model, wherein said output model is based on said information obtained about said object and said analysis of said object's geometry and material requirements (e.g., See ‘697; [0026] – [0027], [0032] and [0067], which collectively disclose a trained ML model using part shape and material needs to create a final print plan or model); and
storing said output model in said knowledgebase corpus database (e.g., See ‘697; [0039] and [0042], which collectively disclose the system storing ML generated optimization command results back into the database for future training).
As per claim 2, ‘697 further discloses that the knowledgebase database is used to train at least one Artificial Intelligence (AI) engine (e.g., See ‘697; [0067]).
As per claim 3, ‘697 further discloses printing said object using said generated output model output (e.g., See ‘697; [0005] and [0067], which collectively disclose the ML outputting instructions that commands the printer to print).
As per claim 4, ‘697 further discloses that the printing is three-dimensional (3D) printing (e.g., See ‘697; [0056] and [0068], which discloses sending the print commands to a 3D printer).
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., See ‘697; [0043] and [0044], which collectively disclose the customer order including part details when the order request is made).
As per claim 10, ‘697 further discloses that the knowledge base includes input from at least one third party individual (e.g., See ‘697; [0042], which discloses other users adding data into the shared database).
As per claim 12, ‘697 further discloses that the object's geometry includes a length, a width, a depth 12. and any angles of connection (e.g., See ‘697; [0050] and [0053], which collectively disclose storing 3D dimensions (X, Y and Z) and angles/offsets for the model or part).
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.
As per claim 19, the rational as set forth above with respect to the rejection of claim 12 is applied herein.
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 (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 with time-based shape change capabilities (e.g., See ‘244; [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’s known 4D printing capability into ‘697’s printing step for the purpose of making printed parts that can change shape over time, and thereby reduce assembly and improve shipping and fabrication efficiency.
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 material for each component specifically to determine material reusability.
‘473 discloses this feature (e.g., See ‘473; [0038], [0049] and [0051], which collectively disclose using collected data to decide next best handling of each component or material, including reuse, recycling, or reprocessing).
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 for the purpose of cutting waste and costs by choosing the best way to reuse, reprocess, or recycle each components material, using data driven predictions so that fewer parts become waste which increases overall profitability.
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., See ‘473; [0047] and [0049], which collectively disclose assessing sustainability performance at the part or subcomponent 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., See ‘473; [0036], [0043] and [0049], which collectively disclose ML assessing component environmental impacts from usage data and guiding reuse or recycling decisions).
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 third party individuals include at least designers, engineers/operators (e.g., See ‘697; [0042], [0047] and [0049]; which describe multiple users contributing to the knowledge base, including a “part designer”, and an operator performing CAD-to-STL conversion/import steps and storing related corrective action information in the knowledge base), ‘697 does not adequately disclose the third party individual to also include printed object component material source providers.
‘024 discloses these features (e.g., See ‘024; [0066], [0067] and [0073], which collectively disclose third party suppliers providing base files and specifications, including part files with specifications from a manufacturer, for use in printing).
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 part 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 instrucitons; 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
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.
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, 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.
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.
/RONALD D HARTMAN JR/Primary Patent Examiner, Art Unit 2119 January 9, 2026
/RDH/