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
Status
This instant application No. 18/033,289 has Claims 1-15 pending.
Priority /Filing Date
Current application, filed on 04/21/2023 is the national stage filing (371) of International application PCT/US20/57031, filed on 10/23/2020. The priority filing date of this application is October 23, 2020.
Information Disclosure Statement
As required by M.P.E.P. 609(C), the Applicant’s submissions of the Information Disclosure Statements dated May 8, 2023 is acknowledged by the Examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609 C(2), a copy of each of the PTOL-1449s initialed and dated by the Examiner is attached to the instant Office action.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
4. Claims 1-15 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-16 of U.S. Patent No. 12,649,281 from the same inventors and assignee. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the ‘281 Patent include all the limitations of this Application as well as additional limitations.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 2A Prong One:
Independent Claim 1 recite
downscaling a slice of a three-dimensional (3D) build to produce a downscaled image;
determining an agent map based on the downscaled image.
Independent Claim 10 recite
generate, an agent map based on the layer image.
Independent Claim 13 recite
Generate an agent map based on a downscaled image of a slice of a three dimensional
(3D) build.
All of the above limitations, as recited, are process steps that cover both a mathematical concept and a mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. Said limitations in Claims 1 10, and 13 are a process that under its broadest reasonable interpretation, covers performance of the limitations in the mind and /or mathematical concept but for the recitation of generic computer components. Other than reciting “a machine learning model”, “a memory”, “a processor coupled to the memory” and “non-transitory tangible computer-readable medium storing executable code” in the claims nothing in the claim elements precludes the steps from practically being performed in the mind and/or mathematical concept. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or a mathematical concept including mathematical calculation/relationship, but for the recitation of generic computer components, then it falls within the “mental processes” and “mathematical concept” groupings of abstract ideas. As such Claims 1, 10, and 13 recite an abstract idea.
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. The claims recite the additional element of “a machine learning model”, “a memory”, “a processor coupled to the memory” and “non-transitory tangible computer-readable medium storing executable code” to perform the claimed steps at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional element of receiving and storing layer image is a data gathering step and is an insignificant pre-solution activity. As such this additional element also does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
Finally, the pre-processing step of receiving and storing layer image is categorized as insignificant extra solution activity under 2106.05(g). Claims 1, 10 and 13 only recite “a machine learning model”, “a memory”, “a processor coupled to the memory” and “non-transitory tangible computer-readable medium storing executable code” to perform the claimed steps and therefore only recite a general purpose computer rather than a specific machine under MPEP 2106.05(b), and are directed to mere instructions to apply the exception under MPEP 2106.05(f), and do not result in anything significantly more than the judicial exception. The additional elements have been considered both individually and as an ordered combination in the significantly more consideration. The inclusion of the computer or memory and controller to perform the determining and generating steps amount to nor more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claims 1, 10, and 13 are not patent eligible.
The dependent claims include the same abstract ideas recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent Claim 2 is directed to further limiting determine the type of sequence used in the agent map, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
Dependent Claims 3 and 4 are directed to further limiting specifying the type of agent map generated, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
Dependent Claim 5 is directed to further limiting applying a perimeter mask to the detailing agent map to produce a masked detailing agent map, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
Dependent Claim 6 is directed to further limiting wherein the machine learning model is trained based on a masked ground truth agent map, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
Dependent Claim 7 is directed to further limiting wherein the masked ground truth agent map is determined based on an erosion or dilation operation on a ground truth agent map, and wherein the method further comprises binarizing the masked ground truth agent map, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
Dependent Claim 8 is directed to further limiting wherein the machine learning model is trained using a loss function that is based on the masked ground truth agent map, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
Dependent Claim 9 is directed to further limiting wherein the machine learning model is a bidirectional convolutional recurrent neural network, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
Dependent Claim 11 is directed to further limiting determining patches based on the layer image; infer agent map patches based on the patches; and combine the agent map patches to produce the agent map, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
Dependent Claim 12 is directed to further limiting performing a rolling window of inferences; and utilize a heuristic to choose one of the inferences as the agent map, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
Dependent Claim 14 is directed to further limiting determining a loss based on a predicted agent map and a ground truth agent map, comprising code to cause the processor to determine a detailing agent loss component and a fusing agent loss component, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
Dependent Claim 15 is directed to further limiting determining a loss based on a masked predicted detailing agent map and a masked predicted fusing agent map; and code to cause the processor to train a machine learning model based on the loss, which further narrows the abstract idea identified in the independent claim, which is directed to “mental processes” and “mathematical concepts.”
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.
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.
6. Claims 1, 3-5, 9-11 and 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zeng et al., hereafter Zeng (Pub. No.: US 2022/0088878 A1).
Regarding Claim 1, Zeng disclose a method, comprising:
downscaling a slice of a three-dimensional (3D) build to produce a downscaled image (Zeng: ([0033]: a contone map may correspond to a two-dimensional (2D) layer (e.g., 2D slice, 2D cross-section, etc.) of the 3D model. For instance, a 3D model may be processed to produce a plurality of contone maps corresponding to a plurality of layers of the 3D model.
In some examples, a contone map may be expressed as a 2D grid of values, where each value may indicate whether to print an agent and/or an amount of agent at the location on the 2D grid. For instance, the location of a value in the 2D grid may correspond to a location in the build area 102 ( e.g. a location (x, y) of a particular level (z) at or above the build area 102); [0041]-[0044] , [0048]: generating a detail contone map using a model (242), for a detailing agent, based on a slice data…… The thermal image may be downsampled into a same resolution as low-resolution thermal sensing (e.g. 42x30 pixels)); and
determining, using a machine learning model, an agent map based on the downscaled image (Zeng: ([0041]-[0044] , [0057]-[0062]: generating a detail contone map using a model (242), for a detailing agent, based on a slice data……..a machine learning model ( e.g., neural network or networks, deep learning, etc.) may be utilized to predict a thermal image. In some approaches, a predicted thermal image of a layer may be computed based on a captured thermal image or images corresponding to a previous layer or layers).
Regarding Claim 3, Zeng further disclose the method of claim 1, wherein the agent map is a fusing agent map (Zeng: ([0033]: a fusing agent contone map indicates coordinates and/or an amount for printing the fusing agent 112).
Regarding Claim 4, Zeng further disclose the method of claim 1, wherein the agent map is a detailing agent map (Zeng: [0033]: In an example, a detailing agent contone map indicates coordinates and/or an amount for printing the detailing agent 120).
Regarding Claim 5, Zeng further disclose the method of claim 4, further comprising applying a perimeter mask to the detailing agent map to produce a masked detailing agent map (Zeng: [0033]: In an example, a detailing agent contone map indicates coordinates and/or an amount for printing the detailing agent 120; Zhang: [0056]: generate a segmentation mask ().
Regarding Claim 9, Zeng further disclose the method of claim 1, wherein the machine learning model is a bidirectional convolutional recurrent neural network (Zeng: [0039]).
Regarding Claim 10, Zeng disclose an apparatus, comprising:
a memory to store a layer image (Zeng: [0033], [0038]: the thermal sensor 106 may capture
a thermal image for each layer of an object or objects being manufactured; [0034]: The data store 114 is a machine-readable storage medium.); and
a processor coupled to the memory, wherein the processor is to generate, using a machine learning model, an agent map based on the layer image (Zeng: ([0041]-[0044] , [0057]-[0062]: generating a detail contone map using a model (242), for a detailing agent, based on a slice data……..a machine learning model ( e.g., neural network or networks, deep learning, etc.) may be utilized to predict a thermal image. In some approaches, a predicted thermal image of a layer may be computed based on a captured thermal image or images corresponding to a previous layer or layers).
Regarding Claim 11, Zeng further disclose the apparatus of claim 10, wherein the processor is to:
determine patches based on the layer image (Zeng: [0033]: a contone map may correspond to a two-dimensional (2D) layer (e.g., 2D slice, 2D cross-section, etc.) of the 3D model);
infer agent map patches based on the patches; and combine the agent map patches to produce the agent map (Zeng: ([0041]-[0044] , [0057]-[0062]: generating a detail contone map using a model (242), for a detailing agent, based on a slice data……..a machine learning model ( e.g., neural network or networks, deep learning, etc.) may be utilized to predict a thermal image. In some approaches, a predicted thermal image of a layer may be computed based on a captured thermal image or images corresponding to a previous layer or layers).
Regarding Claim 13, Zeng further disclose the non-transitory tangible computer-readable medium storing executable code, comprising:
code to cause a processor to generate, using a machine learning model, an agent map based on a downscaled image of a slice of a three dimensional (3D) build (Zeng: [0034], [0041]-[0044] , [0048]: generating a detail contone map using a model (242), for a detailing agent, based on a slice data…… The thermal image may be downsampled into a same resolution as low-resolution thermal sensing (e.g., 42x30 pixels); [0041]-[0044] , [0057]-[0062]: a machine learning model ( e.g., neural network or networks, deep learning, etc.) may be utilized to predict a thermal image. In some approaches, a predicted thermal image of a layer may be computed based on a captured thermal image or images corresponding to a previous layer or layers).
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.
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 of this title, 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 obvious over Zeng et al., hereafter Zeng (Pub. No.: US 2022/0088878 A1), in view of Bryan John Chasko hereafter Chasko (Pub. No.: US 2020/0251220 A1).
Regarding Claim 2, Zeng do not explicitly disclose:
determining a lookahead sequence, a current sequence, and a lookback sequence, wherein determining the agent map is based on the lookahead sequence, the current sequence, and the lookback sequence
Chasko discloses:
determining a lookahead sequence, a current sequence, and a lookback sequence, wherein determining the agent map is based on the lookahead sequence, the current sequence, and the lookback sequence (Chasko: Figures 4, 5, [0026]-[0031], [0043]-[0045]: Examiner’s Remark(ER): the sequences described in the cited paragraphs could be construed as claimed lookahead , current and lookback sequences because of their interaction with the agent in different states).
Zeng and Chasko are analogous art because they are from the same field of endeavor. They both relate to 3D model development.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the above 3D model development application, as taught by Zeng, and incorporating the use of different sequences, as taught by Chasko.
One of ordinary skill in the art would have been motivated to do this modification in order to facilitate map generation base on the features.
8. Claims 6-8 and 14-15 are rejected under 35 U.S.C. 103 as being obvious over Zeng et al., hereafter Zeng (Pub. No.: US 2022/0088878 A1), in view of Zhang et al., hereafter Zhang (Pub. No.: US 2019/0171871 A1).
Regarding Claim 6, Zeng do not explicitly disclose:
wherein the machine learning model is trained based on a masked ground truth agent map.
Zhang disclose:
wherein the machine learning model is trained based on a masked ground truth agent map (Zhang: ([0054]-[0059], [0078]-[0082]: model is trained with ground truth image sample).
Zeng and Zhang are analogous art because they are from the same field of endeavor. They both relate to feature map generation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the above 3D model development application, as taught by Zeng, and incorporating the use of masked ground truth agent map, as taught by Zhang.
One of ordinary skill in the art would have been motivated to do this modification in order to facilitate map generation base on the features.
Regarding Claim 7, the combinations of Zing and Zhang further disclose the method of claim 6, wherein the masked ground truth agent map is determined based on an erosion or dilation operation on a ground truth agent map, and wherein the method further comprises binarizing the masked ground truth agent map (Zhang: ([0054]-[0059],[0078]-[0082]: generate a segmentation mask (e.g., which may be represented as a matrix or grid with binary values that indicate whether a corresponding pixel belongs to a detected instance of the object or not), compare the generated mask with a ground-truth mask (e.g., indicating the true pixels belonging to the object)).
Motivation to combine Zeng and Zhang is same here as Claim 6.
Regarding Claim 8, the combinations of Zing and Zhang further disclose the method of claim 6, wherein the machine learning model is trained using a loss function that is based on the masked ground truth agent map (Zhang: [0078]: The system may then compare results based on the second combined regional feature map to the corresponding ground truths, and use the comparison results (e.g., as defined by loss functions) to update the neural network).
Motivation to combine Zeng and Zhang is same here as Claim 6.
Regarding Claim 14, Zeng do not explicitly disclose:
determine a loss based on a predicted agent map and a ground truth agent map, comprising code to cause the processor to determine a detailing agent loss component and a fusing agent loss component.
Zhang disclose:
determine a loss based on a predicted agent map and a ground truth agent map, comprising code to cause the processor to determine a detailing agent loss component and a fusing agent loss component (Zhang: ([0054]-[0059],[0078]-[0082]: model is trained with ground truth image sample…. The system may then compare results based on the second combined regional feature map to the corresponding ground truths, and use the comparison results (e.g., as defined by loss functions) to update the neural network).
Motivation to combine Zeng and Zhang is same here as Claim 6.
Regarding Claim 15, Zeng do not explicitly disclose:
determine a loss based on a masked predicted detailing agent map and a masked predicted fusing agent map.
Zhang disclose:
determine a loss based on a masked predicted detailing agent map and a masked predicted fusing agent map (Zhang: [0078]: The system may then compare results based on the second combined regional feature map to the corresponding ground truths, and use the comparison results (e.g., as defined by loss functions) to update the neural network).; and
code to cause the processor to train a machine learning model based on
the loss (Zhang: [0078]: The system may then compare results based on the second combined regional feature map to the corresponding ground truths, and use the comparison results (e.g., as defined by loss functions) to update the neural network).
Motivation to combine Zeng and Zhang is same here as Claim 6.
9. Claim 12 is rejected under 35 U.S.C. 103 as being obvious over Zeng et al., hereafter Zeng (Pub. No.: US 2022/0088878 A1), in view of Michael Klosch-Trageser, hereafter Klosch-Trageser (Pub. No.: US 2020/0130280 A1).
Regarding Claim 12, Zeng do not explicitly disclose:
perform a rolling window of inferences; and utilize a heuristic to choose one of the inferences as the agent map.
Klosch-Trageser disclose:
perform a rolling window of inferences (Klosch-Trageser: [0024]-[0027], [0034]: process window card); and
utilize a heuristic to choose one of the inferences as the agent map (Klosch-Trageser: [0024]-[0027], [0034]: process window card).
Zeng and Klosch-Trageser are analogous art because they are from the same field of endeavor. They both relate to feature map generation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the above 3D model development application, as taught by Zeng, and incorporating the use of process window card, as taught by Klosch-Trageser.
One of ordinary skill in the art would have been motivated to do this modification in order to facilitate map generation base on the features
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Luan et al. (Pub. No.: US 2021/0247735 A1) disclose methods for thermal mapping by an electronic device where in a map and a first thermal image at a first resolution is obtained. a neural network is used to determine a second thermal image at a second resolution based on the map and the first thermal image. The second resolution is greater than the first resolution.
James Elmer ABBOTT, JR. (Pub. No.: US 2017 /0197366 A1) teaches objects based on data in a 3D model of an object created a CAD computer program product. The model data is processed into slices each defining that part of a layer or layers of build material to be solidified..
Hwang et al. (Pub. No.: US 2019/0283333 A1) teaches systems and methods of monitoring solidification quality and automatic correcting any detected error in additive manufacturing.
Mark Christian Messner (Pub. No.: US 2019/0061269 A1) conceptually presents generating slices of slender member structures for additive manufacturing of object.
Prasad et al. (Pub. No.: US 2017 /0151722 A1) defines a computational modeling method for identifying how to apply a modifying agent during a three-dimensional (3D) printing method, a thermal diffusion model of a layer of a 3D object to be formed from a portion of a sinterable material using the 3D printing method is created.
11. Examiner’s Remarks: Examiner has cited particular columns and line numbers in the references 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 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 their 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. In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Correspondence Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IFTEKHAR A KHAN whose telephone number is (571)272-5699. The examiner can normally be reached on M-F from 9:00AM-6:00PM (CST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached on (571)272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IFTEKHAR A KHAN/Primary Examiner, Art Unit 2187
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