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 .
DETAILED ACTION
This is a Final Office Action of the instant application 17/942,436 (hereinafter the ‘436 application) filed on 9/22/2022, responsive to the Amendment filed 2/13/2026.
Claim Rejections - 35 USC § 112
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 1-18 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 claim(s) contains 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.
[FOR CLAIMS 1-6] Specifically, there is no support provided nor could support be found in the specification “to process the converted digital component design model as a top branch of a neural network model and the one or more design data attributes as a bottom branch of the neural network model”.
[FOR CLAIMS 7-12] Specifically, there is no support provided nor could support be found in the specification for “a voxelated data format for use with one or more tabular design data attributes”.
[FOR CLAIMS 13-18] Specifically, there is no support provided nor could support be found in the specification for “generate design data”.
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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over ReMine et al., U.S. Publication No. 2020/0159886, hereinafter ReMine, Steingrimsson et al., U.S. Publication No. 2020/0257933, hereinafter Steingrimsson, Rittman et al., U.S. Patent No. 11,809,797, hereinafter Rittman, and Cunningham, U.S. Publication No. 2024/0311612.
With regard to claims 1, 7, and 13, which teach a system, method, and component “for designing a component, comprising: a component design system operating on a processor that is configured to execute one or more algorithms to generate a digital component design model;” ReMine teaches, in 27-28 and 36, a system, method, and apparatus for artificial intelligence based part design, where parts are initially input into the system via computer-aided design (CAD).
With regard to claims 1, 7, and 13, which further teach “a graphic information unitization system operating on the processor that is configured to execute one or more algorithms to receive the digital component design model and to convert it into a format for use with one or more design data attributes;” ReMine teaches, in paragraphs 36 and 42, the AI design system taking in the CAD design along with user supplied desired design improvements / attributes and converting the design into an encoded format (voxelization format / mesh model) usable by the AI based design system.
With regard to claims 1, 7, and 13, which further teach “a deep learning system operating on the processor that is configured to execute one or more algorithms to receive the converted digital component design model and the one or more design data attributes and to process the converted digital component design model and the one or more design data attributes using a deep learning algorithm to identify one or more design modifications,” ReMine teaches, in paragraph 39, after the encoded realized part designs are input into the space, the neural network (machine learning algorithm) trains the space, then generates newly imagined part designs. The system further uses the real metadata regarding initial designs associated with realized parts and determines associated metadata for imagined parts (cost to manufacture, manufacturability, structural strength, weight, etc.) (see paragraph 40). Finally the system provides the user with “an optimal part design… based on the user’s initial desired part design and the user’s objectives for the desired part design” (see paragraph 42). With regard to converting the model in to a voxelized data, ReMine teaches, in paragraph 36, the inputted parts being converted in to a voxelization format, where at least one processor converts (e.g., voxelizes) each realized part design (CAD model) into a voxelization format.
ReMine teaches using artificial intelligence to generate newly imagined part designs, supra, but doesn’t specifically teach use of deep learning in the creation of new parts. Steingrimsson teaches a system for using machine learning to accelerate the design process and generate new designs (see paragraphs 5, 16, and 17), similar to that of ReMine, but further specifically utilizes deep learning in the creation of new designs (see paragraphs 257-258). Here the AI Machine Learning system of Steingrimsson is described to further rely upon Deep Learning in its design creation to provide multilayer learning and evaluation trained on large quantities of data. It would be obvious to one of ordinary skill in the art at the time to utilize the deep learning algorithms of Steingrimsson in the machine learning system of ReMine as they allow for a greater knowledge set to be utilized while enabling fast recognition of complex patterns, enabling the system to maximize the designs effectiveness. Furthermore ReMine utilizes Neural Networks which implies the use of Deep Learning.
ReMine and Steingrimsson teach the AI assisted creation of parts, but are not specific to electrical components of a server. Rittman teaches the use of AI and machine learning for design and manufacturing (see paragraph 2, lines 27-40; supported by page 1 of Provisional 63/393,959), but further specifically teaches use of neural networks that self-train to create 3D shapes that optimized surface area of Integrated Circuits (IC) in a way that has the highest silicon yield, area utilization, largest surface area, and most optimal thermal dissipation (see paragraph 2, lines 27-67; supported by pages 1-2 of Provisional 63/393,959). It would be obvious to one of ordinary skill in the art at the time to utilize the process of design, perfected via deep learning algorithms, of ReMine and Steingrimsson, in the IC design system of Rittman to maximize the work of engineers working on the design through the AI integration. Here an initial design can be modified and tested iteratively, taking into account all aspects of manufacturing and design constraints, to quickly generate new ideal designs for manufacture.
The combination of ReMine, Steingrimsson, and Rittman disclose use of neural networks in image data analysis and further in design attributes, but don’t specifically teach the use of them together with the design model as a top branch and the design attributes in a bottom branch. Cunningham teaches the analysis of both image data and tabular attribute data, similar to that of ReMine, Steingrimsson, and Rittman, but further specifically teaches use of a convolution neural network (CNN) on image data on a top branch of a learning algorithm and an artificial neural network (ANN) on non-image (tabular) data on a bottom branch of a learning algorithm (see paragraphs 17, 27-30 and figures 3A and 3B. It would be obvious to one of ordinary skill in the art at the time to utilize the combined learning algorithms, Cunningham in the systems of ReMine, Steingrimsson, and Rittman, in order to best deal with image date (best analyzed in a CNN) and tabular data (best analyzed in a ANN) to develop an output based on both datapoints.
With regard to claims 2, 8, and 14, which teach wherein the component design system comprises a computer aided design modeling system, ReMine teaches, in paragraph 36, parts being initially inputted into the system via computer-aided design (CAD).
With regard to claims 3, 9, and 15, which teach wherein the graphic information unitization system comprises a voxelization system and the design data attributes comprise tabular data, ReMine teaches, in paragraph 36, the inputted parts being converted in to a voxelization format, where at least one processor converts (e.g., voxelizes) each realized part design (CAD model) into a voxelization format. Further see the rejection to claims 1, 7, and 13.
With regard to claims 4, 10, and 16, which teach wherein the bottom branch of the neural network model comprises an artificial neural network, ReMine teaches, in paragraph 39, generating new optimal parts via a “neural network (e.g. a machine learning algorithm) (e.g. an autoencoder, such as a three-dimensional (3D) convolutional autoencoder)”. Steingrimsson further discusses the use of artificial neural networks for part design (see abstract and paragraphs 286 and 375). Further see the rejection to claims 1, 7, and 13.
With regard to claims 5, 11, and 17, which teach wherein the top branch of the neural network model comprises a convolutional neural network, ReMine teaches, in paragraph 39, generating new optimal parts via a “neural network (e.g. a machine learning algorithm) (e.g. an autoencoder, such as a three-dimensional (3D) convolutional autoencoder)”. Steingrimsson further discusses the use of convolutional neural networks for part design (see paragraph 44). Further see the rejection to claims 1, 7, and 13.
With regard to claims 6, 12, and 18, which teach wherein the one or more design data attributes comprise an ambient temperature, ReMine teaches, in paragraph 40, evaluating the strength of the product. While Steingrimsson further includes in the consideration process parameters such as “ambient temperature” (see paragraph 21 and table 1), further noting to “predict material compositions of optimal material functionality correlated to manufacturing process conditions” (see paragraph 28). Rittman further notes use of the neural network to create 3D shapes have optimal thermal dissipation (see paragraph 2, lines 27-67; supported by pages 1-2 of Provisional 63/393,959). Given each of these references are in the same art space it would be obvious to use similar techniques for meeting the given desired attribute of idea ambient temperature.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on the same combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Summary
Claims 1-18 are REJECTED.
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 DENNIS G BONSHOCK whose telephone number is (571)272-4047. The examiner can normally be reached M-F 7:15 - 4:45.
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/DENNIS G BONSHOCK/Primary Examiner, Art Unit 3992